Reporting modules

ABSTRACT

Methods and apparatus, including computer program products, are provided for processing analyte data. In some exemplary implementations, there is provided a method. The method may include generating, by at least one processor, a view comprising an abstraction distilled from the sensor data over a time period. The view may further comprise a graphical representation comprising a plurality of different graphically distinct elements representative of whether the abstraction over the time period is at least one of at, above, or within a predetermined glucose concentration level for a host; a call out comprising value help for the graphical representation, and a textual legend comprising a description of the graphical representation and the abstraction. The method may further include providing the view as a module. Related systems, methods, and articles of manufacture are also disclosed.

INCORPORATION BY REFERENCE TO RELATED APPLICATION

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 13/789,341 filed on Mar. 7, 2013, which is a continuation of U.S.application Ser. No. 13/788,375 filed on Mar. 7, 2013, which claims thebenefit of U.S. Provisional Patent Application No. 61/655,991 filed onJun. 5, 2012. The disclosures of each of the aforementioned applicationsare hereby expressly incorporated by reference in their entirety and arehereby expressly made a portion of this application.

FIELD

The present disclosure generally relates to data processing of glucosedata of a host.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin, such as in the case of Type I diabetes and/or inwhich insulin is not effective, such as Type 2 diabetes. In a diabeticstate, a victim suffers from high blood sugar, which causes an array ofphysiological derangements, such as kidney failure, skin ulcers, orbleeding into the vitreous of the eye, associated with the deteriorationof small blood vessels. A hypoglycemic reaction, such as low bloodsugar, may be induced by an inadvertent overdose of insulin, or after anormal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

A diabetic person may carry a self-monitoring blood glucose (SMBG)monitor, which typically requires uncomfortable finger pricking methods.Due to the lack of comfort and convenience, a diabetic typicallymeasures his or her glucose level only two to four times per day.Unfortunately, these time intervals are spread so far apart that thediabetic will likely find out too late, sometimes incurring dangerousside effects, of a hyperglycemic or hypoglycemic condition. In fact, itis not only unlikely that a diabetic will take a timely SMBG value, butadditionally the diabetic will not know if his blood glucose value ishigher or lower based on conventional methods.

Consequently, a variety of non-invasive, transdermal (e.g.,transcutaneous) and/or implantable electrochemical sensors are beingdeveloped for continuously detecting and/or quantifying blood glucosevalues. These devices generally transmit raw or minimally processed datafor subsequent analysis at a remote device, which can include a display,to allow presentation of information to a user hosting the sensor.

SUMMARY

Methods and apparatus, including computer program products, are providedfor processing analyte data. In some exemplary implementations, there isprovided a method. The method may include generating, by at least oneprocessor, a view comprising an abstraction distilled from the sensordata over a time period. The view may further comprise a graphicalrepresentation comprising a plurality of different graphically distinctelements representative of whether the abstraction over the time periodis at least one of at, above, or within a predetermined glucoseconcentration level for the host, a call out comprising value help forthe graphical representation, and a textual legend comprising adescription of the graphical representation and the abstraction. Themethod may further include providing the view as a module.

In some exemplary implementations, there is provided an apparatusincluding at least one processor and at least one memory including codewhich when executed by the at least one processor provides at leastgenerating a view comprising an abstraction distilled from sensor dataover a time period and providing the view as a module. The view mayfurther include a graphical representation comprising a plurality ofdifferent graphically distinct elements representative of glucosevariability including a rate of change of the sensor data over the timeperiod, a call out comprising value help for the graphicalrepresentation, and a textual legend comprising a description of thegraphical representation and the abstraction.

In some exemplary implementations, there is provided a non-transitorycomputer-readable storage medium including program code, which whenexecuted by at least one processor provides at least generating at leastone of a plurality of reporting modules configured to provide a viewcomprising an abstraction distilled from sensor data over a time periodand providing the view as the at least one of a plurality of reportingmodules. The sensor data may represent a glucose concentration level fora host. The view may include a graphical representation comprising aplurality of different graphically distinct elements representative ofthe glucose concentration level for the host, a call out comprisingvalue help for the graphical representation, and a textual legendcomprising a description of the graphical representation and theabstraction.

In some implementations, the above-noted aspects may further includeadditional features described herein including one or more of thefollowing. The time frame may include at least one of the following: 8hours, 1 day, 2 days, 7 days, or 30 days. The graphically distinctelements may use at least one of different shading or position torepresent whether the abstraction over the time period is at least oneof at, above, or within the predetermined glucose concentration levelfor the host. The call out may include the value help coupled to one ofthe different graphically distinct elements and provide a numericalvalue for the one of the different graphically distinct elements. Theview may be configured for presentation as a single page within the userinterface. The module may include metadata. The metadata may includeinformation to enable selection of the module from among a plurality ofmodules. The view may be presented as well. wherein the graphicallydistinct elements are representative of whether the abstraction over thetime period is at least one of at, above, or within a predeterminedglucose concentration level for the host. The graphically distinctelements may be representative of glucose variability over the timeperiod. At least one of the plurality of reporting modules may includean insights module including at least one of a plurality of insightsdetected based on a pattern and selected for presentation based on aranking according to at least one of a user preference, a quantity ofdevices providing data, one or more types of devices providing data, anda correlation with the host. At least one of the plurality of reportingmodules may include a compare with module presenting a statisticalcomparison between the host and a group selected based on aggregate datarepresentative of one or more of the following: a diabetes typeassociated with the group, an age associated with the group, a genderassociated with the group, an age of diagnosis associated with thegroup, a location associated with the group, and a treatment facilityassociated with the group. At least one of the plurality of reportingmodules may include at least one of a patient information module, ahighlights module, a high and low period of glucose module, a devicesused module, a compared with module, a daily summary module, a weeklysummary module, an over time summary module, a continuous glucose levelsmodule, a report legend module, and a testing frequency module.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive. Further features and/or variations may beprovided in addition to those set forth herein. For example, theimplementations described herein may be directed to various combinationsand subcombinations of the disclosed features and/or combinations andsubcombinations of several further features disclosed below in thedetailed description.

DESCRIPTION OF THE DRAWINGS

In the drawings,

FIG. 1 depicts a diagram illustrating a continuous analyte sensor systemincluding a sensor electronics module in accordance with some exemplaryimplementations;

FIG. 2 depicts a block diagram illustrating the sensor electronicsmodule in accordance with some exemplary implementations;

FIG. 3A depicts a block diagram illustrating the sensor electronicsmodule communicating with multiple sensors, including a glucose sensorin accordance with some exemplary implementations;

FIG. 3B depicts a perspective view of a sensor system including amounting unit and sensor electronics module attached thereto inaccordance with some exemplary implementations;

FIG. 3C depicts a side view of the sensor system of FIG. 3B;

FIG. 4A depicts a block diagram of an analyte processing system inaccordance with some exemplary implementations;

FIG. 4B depicts another block diagram of an analyte processing system inaccordance with some exemplary implementations;

FIG. 5 depicts a block diagram of a calculation engine configured togenerate counts representative of analyte levels in a host in accordancewith some exemplary implementations;

FIG. 6A-1 depict an example of a report template generated in accordancewith some exemplary implementations;

FIG. 6A-2 depicts an example process of dynamically selecting reportmodules in accordance with some exemplary implementations;

FIGS. 6A-3 through 6A-28 depict examples of report modules in accordancewith some exemplary implementations;

FIGS. 7-10 depict additional examples of report modules in accordancewith some exemplary implementations; and

FIG. 11 depicts an example of a process for processing analyte data inaccordance with some exemplary implementations.

Like labels are used to refer to same or similar items in the drawings.

DETAILED DESCRIPTION

FIG. 1 depicts an example system 100, in accordance with some exemplaryimplementations. The system 100 includes a continuous analyte sensorsystem 8 including a sensor electronics module 12 and a continuousanalyte sensor 10. The system 100 may include other devices and/orsensors, such as medicament delivery pump 2 and glucose meter 4. Thecontinuous analyte sensor 10 may be physically connected to sensorelectronics module 12 and may be integral with (e.g., non-releasablyattached to) or releasably attachable to the continuous analyte sensor10. The sensor electronics module 12, medicament delivery pump 2, and/orglucose meter 4 may couple with one or more devices, such as displaydevices 14, 16, 18, and/or 20.

In some exemplary implementations, the system 100 may include acloud-based analyte processor 490 configured to analyze analyte data(and/or other patient related data) provided via network 406 (e.g., viawired, wireless, or a combination thereof) from sensor system 8 andother devices, such as display devices 14-20 and the like, associatedwith the host (also referred to as a patient) and generate reportsproviding high-level information, such as statistics, regarding themeasured analyte over a certain time frame.

In some exemplary implementations, cloud-based analyte processor 490 mayprovide reporting modules. For example, analyte processor 490 or areport generator therein may generate a view including an abstractiondistilled from the sensor data over a time period. The view may furtherinclude a graphical representation comprising a plurality of differentgraphically distinct elements representative of whether the abstractionover the time period is at least one of at, above, or within apredetermined glucose concentration level for the host; a call outcomprising value help for the graphical representation, and a textuallegend comprising a description of the graphical representation and theabstraction. The analyte processor 490 and/or report generator mayfurther provide the view as a module.

In some exemplary implementations, system 100 may generate reportsdynamically. For example, the analyte processor 490 may receive arequest to generate a report. In response to the request, the analyteprocessor 490 may then select at least one module from among a pluralityof modules. This selection may be performed based on metadata. Themetadata may include information representative of the host, the type ofdevice being used to measure the glucose concentration level, rules, andthe like. The selection may be considered dynamic in the sense thatmodule selection varies for each request based on metadata. The reportmay then be generated to include the at least one selected module andthen provided to a user interface for presentation.

Before providing additional details regarding the cloud-based analyteprocessing system disclosed herein, the following provides a detaileddescription of the sensors and systems that may provide data to thecloud-based processing system disclosed herein.

In some exemplary implementations, the sensor electronics module 12 mayinclude electronic circuitry associated with measuring and processingdata generated by the continuous analyte sensor 10. This generatedcontinuous analyte sensor data may also include algorithms, which can beused to process and calibrate the continuous analyte sensor data,although these algorithms may be provided in other ways as well. Thesensor electronics module 12 may include hardware, firmware, software,or a combination thereof to provide measurement of levels of the analytevia a continuous analyte sensor, such as a continuous glucose sensor. Anexample implementation of the sensor electronics module 12 is describedfurther below with respect to FIG. 2.

The sensor electronics module 12 may, as noted, couple (e.g., wirelesslyand the like) with one or more devices, such as display devices 14, 16,18, and/or 20. The display devices 14, 16, 18, and/or 20 may beconfigured for presenting (and/or alarming) information, such as sensorinformation transmitted by the sensor electronics module 12 for displayat the display devices 14, 16, 18, and/or 20.

The display devices may include a relatively small, key fob-like displaydevice 14, a relatively large, hand-held display device 16, a cellularphone (e.g., a smart phone, a tablet, and the like), a computer 20,and/or any other user equipment configured to at least presentinformation (e.g., a medicament delivery information, discreteself-monitoring glucose readings, heart rate monitor, caloric intakemonitor, and the like).

In some exemplary implementations, the relatively small, key fob-likedisplay device 14 may comprise a wrist watch, a belt, a necklace, apendent, a piece of jewelry, an adhesive patch, a pager, a key fob, aplastic card (e.g., credit card), an identification (ID) card, and/orthe like. This small display device 14 may include a relatively smalldisplay (e.g., smaller than the large display device) and may beconfigured to display certain types of displayable sensor information,such as a numerical value and an arrow.

In some exemplary implementations, the relatively large, hand-helddisplay device 16 may comprise a hand-held receiver device, a palm-topcomputer, and/or the like. This large display device may include arelatively larger display (e.g., larger than the small display device)and may be configured to display information, such as a graphicalrepresentation of the continuous sensor data including current andhistoric sensor data output by sensor system 8.

In some exemplary implementations, the continuous analyte sensor 10comprises a sensor for detecting and/or measuring analytes, and thecontinuous analyte sensor 10 may be configured to continuously detectand/or measure analytes as a non-invasive device, a subcutaneous device,a transdermal device, and/or an intravascular device. In some exemplaryimplementations, the continuous analyte sensor 10 may analyze aplurality of intermittent blood samples, although other analytes may beused as well.

In some exemplary implementations, the continuous analyte sensor 10 maycomprise a glucose sensor configured to measure glucose in the bloodusing one or more measurement techniques, such as enzymatic, chemical,physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, immunochemical, and the like.In implementations in which the continuous analyte sensor 10 includes aglucose sensor, the glucose sensor may be comprise any device capable ofmeasuring the concentration of glucose and may use a variety oftechniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescent monitoring), toprovide a data, such as a data stream, indicative of the concentrationof glucose in a host. The data stream may be raw data signal, which isconverted into a calibrated and/or filtered data stream used to providea value of glucose to a host, such as a user, a patient, or a caretaker(e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse,or any other individual that has an interest in the wellbeing of thehost). Moreover, the continuous analyte sensor 10 may be implanted as atleast one of the following types of sensors: an implantable glucosesensor, a transcutaneous glucose sensor, implanted in a host vessel orextracorporeally, a subcutaneous sensor, a refillable subcutaneoussensor, an intravascular sensor.

Although the description herein refers to some implementations thatinclude a continuous analyte sensor 10 comprising a glucose sensor, thecontinuous analyte sensor 10 may comprises other types of analytesensors as well. Moreover, although some implementations refer to theglucose sensor as an implantable glucose sensor, other types of devicescapable of detecting a concentration of glucose and providing an outputsignal representative of glucose concentration may be used as well.Furthermore, although the description herein refers to glucose as theanalyte being measured, processed, and the like, other analytes may beused as well including, for example, ketone bodies (e.g., acetone,acetoacetic acid and beta hydroxybutyric acid, lactate, etc.), glucagon,Acetyl Co A, triglycerides, fatty acids, intermediaries in the citricacid cycle, choline, insulin, cortisol, testosterone, and the like.

FIG. 2 depicts an example of a sensor electronics module 12, inaccordance with some exemplary implementations. The sensor electronicsmodule 12 may include sensor electronics that are configured to processsensor information, such as sensor data, and generate transformed sensordata and displayable sensor information. For example, the sensorelectronics module may transform sensor data into one or more of thefollowing: filtered sensor data (e.g., one or more filtered analyteconcentration values), raw sensor data, calibrated sensor data (e.g.,one or more calibrated analyte concentration values), rate of changeinformation, trend information, rate of acceleration information, sensordiagnostic information, location information (which may be provided by alocation module 269 providing location information, such as globalpositioning system information), alarm/alert information, calibrationinformation, smoothing and/or filtering algorithms of sensor data,and/or the like.

In some exemplary implementations, the sensor electronics module 12 maybe configured to calibrate the sensor data, and the data storage memory220 may store the calibrated sensor data points as transformed sensordata. Moreover, the sensor electronics module 12 may be configured, insome exemplary implementations, to wirelessly receive calibrationinformation from a display device, such as devices 14, 16, 18, and/or20, to enable calibration of the sensor data from sensor 12 and dataline 212. Furthermore, the sensor electronics module 12 may beconfigured to perform additional algorithmic processing on the sensordata (e.g., calibrated and/or filtered data and/or other sensorinformation), and the data storage memory 220 may be configured to storethe transformed sensor data and/or sensor diagnostic informationassociated with the algorithms.

In some exemplary implementations, the sensor electronics module 12 maycomprise an application-specific integrated circuit (ASIC) 205 coupledto a user interface 122. The ASIC 205 may further include a potentiostat210, a telemetry module 232 for transmitting data from the sensorelectronics module 12 to one or more devices, such devices 14, 16, 18,and/or 20, and/or other components for signal processing and datastorage (e.g., processor module 214 and data store 220). Although FIG. 2depicts ASIC 205, other types of circuitry may be used as well,including field programmable gate arrays (FPGA), one or moremicroprocessors configured to provide some (if not all of) theprocessing performed by the sensor electronics module 12, analogcircuitry, digital circuitry, or a combination thereof.

In the example depicted at FIG. 2, the potentiostat 210 is coupled to acontinuous analyte sensor 10, such as a glucose sensor, via data line212 to receive sensor data from the analyte. The potentiostat 210 mayalso provide via data line 212 a voltage to the continuous analytesensor 10 to bias the sensor for measurement of a value (e.g., a currentand the like) indicative of the analyte concentration in a host (alsoreferred to as the analog portion of the sensor). The potentiostat 210may have one or more channels (and corresponding one or more data lines212), depending on the number of working electrodes at the continuousanalyte sensor 10.

In some exemplary implementations, the potentiostat 210 may include aresistor that translates a current value from the sensor 10 into avoltage value, while in some exemplary implementations, acurrent-to-frequency converter may also be configured to integratecontinuously a measured current value from the sensor 10 using, forexample, a charge-counting device. In some exemplary implementations, ananalog-to-digital converter may digitize the analog signal from thesensor 10 into so-called “counts” to allow processing by the processormodule 214. The resulting counts may be directly related to the currentmeasured by the potentiostat 210, which may be directly related to ananalyte level, such as a glucose level, in the host.

The telemetry module 232 may be operably connected to processor module214 and may provide the hardware, firmware, and/or software that enablewireless communication between the sensor electronics module 12 and oneor more other devices, such as display devices, processors, networkaccess devices, and the like. A variety of wireless radio technologiesthat can be implemented in the telemetry module 232 include Bluetooth,Bluetooth Low-Energy, ANT, ZigBee, IEEE 802.11, IEEE 802.16, cellularradio access technologies, radio frequency (RF), infrared (IR), pagingnetwork communication, magnetic induction, satellite data communication,spread spectrum communication, frequency hopping communication, nearfield communications, and/or the like. In some exemplaryimplementations, the telemetry module 232 comprises a Bluetooth chip,although the Bluetooth technology may also be implemented in acombination of the telemetry module 232 and the processor module 214.

The processor module 214 may control the processing performed by thesensor electronics module 12. For example, the processor module 214 maybe configured to process data (e.g., counts), from the sensor, filterthe data, calibrate the data, perform fail-safe checking, and/or thelike.

In some exemplary implementations, the processor module 214 may comprisea digital filter, such as for example an infinite impulse response (IIR)or a finite impulse response (FIR) filter. This digital filter maysmooth a raw data stream received from sensor 10, data line 212 andpotentiostat 210 (e.g., after the analog-to-digital conversion of thesensor data). Generally, digital filters are programmed to filter datasampled at a predetermined time interval (also referred to as a samplerate). In some exemplary implementations, such as when the potentiostat210 is configured to measure the analyte (e.g., glucose and the like) atdiscrete time intervals, these time intervals determine the samplingrate of the digital filter. In some exemplary implementations, thepotentiostat 210 is configured to measure continuously the analyte, forexample, using a current-to-frequency converter. In thesecurrent-to-frequency converter implementations, the processor module 214may be programmed to request, at predetermined time intervals(acquisition time), digital values from the integrator of thecurrent-to-frequency converter. These digital values obtained by theprocessor module 214 from the integrator may be averaged over theacquisition time due to the continuity of the current measurement. Assuch, the acquisition time may be determined by the sampling rate of thedigital filter.

The processor module 214 may further include a data generator configuredto generate data packages for transmission to devices, such as thedisplay devices 14, 16, 18, and/or 20. Furthermore, the processor module215 may generate data packets for transmission to these outside sourcesvia telemetry module 232. In some exemplary implementations, the datapackages may, as noted, be customizable for each display device, and/ormay include any available data, such as a time stamp, displayable sensorinformation, transformed sensor data, an identifier code for the sensorand/or sensor electronics module, raw data, filtered data, calibrateddata, rate of change information, trend information, error detection orcorrection, and/or the like.

The processor module 214 may also include a program memory 216 and othermemory 218. The processor module 214 may be coupled to a communicationsinterface, such as a communication port 238, and a source of power, suchas a battery 234. Moreover, the battery 234 may be further coupled to abattery charger and/or regulator 236 to provide power to sensorelectronics module 12 and/or charge the batteries 234.

The program memory 216 may be implemented as a semi-static memory forstoring data, such as an identifier for a coupled sensor 10 (e.g., asensor identifier (ID)) and for storing code (also referred to asprogram code) to configure the ASIC 205 to perform one or more of theoperations/functions described herein. For example, the program code mayconfigure processor module 214 to process data streams or counts,filter, calibrate, perform fail-safe checking, and the like.

The memory 218 may also be used to store information. For example, theprocessor module 214 including memory 218 may be used as the system'scache memory, where temporary storage is provided for recent sensor datareceived from data line 212 and potentiostat 210. In some exemplaryimplementations, the memory may comprise memory storage components, suchas read-only memory (ROM), random-access memory (RAM), dynamic-RAM,static-RAM, non-static RAM, easily erasable programmable read onlymemory (EEPROM), rewritable ROMs, flash memory, and the like.

The data storage memory 220 may be coupled to the processor module 214and may be configured to store a variety of sensor information. In someexemplary implementations, the data storage memory 220 stores one ormore days of continuous analyte sensor data. For example, the datastorage memory may store 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,20, and/or 30 (or more days) of continuous analyte sensor data receivedfrom sensor 10 via data line 212. The stored sensor information mayinclude one or more of the following: a time stamp, raw sensor data (oneor more raw analyte concentration values), calibrated data, filtereddata, transformed sensor data, and/or any other displayable sensorinformation.

The user interface 222 may include a variety of interfaces, such as oneor more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228,an audio transducer (e.g., speaker) 230, a backlight, and/or the like.The components that comprise the user interface 222 may provide controlsto interact with the user (e.g., the host). One or more buttons 224 mayallow, for example, toggle, menu selection, option selection, statusselection, yes/no response to on-screen questions, a “turn off” function(e.g., for an alarm), a “snooze” function (e.g., for an alarm), a reset,and/or the like. The LCD 226 may provide the user with, for example,visual data output. The audio transducer 230 (e.g., speaker) may provideaudible signals in response to triggering of certain alerts, such aspresent and/or predicted hyperglycemic and hypoglycemic conditions. Insome exemplary implementations, audible signals may be differentiated bytone, volume, duty cycle, pattern, duration, and/or the like. In someexemplary implementations, the audible signal may be configured to besilenced (e.g., snoozed or turned off) by pressing one or more buttons224 on the sensor electronics module and/or by signaling the sensorelectronics module using a button or selection on a display device(e.g., key fob, cell phone, and/or the like).

Although audio and vibratory alarms are described with respect to FIG.2, other alarming mechanisms may be used as well. For example, in someexemplary implementations, a tactile alarm is provided including apoking mechanism configured to “poke” the patient in response to one ormore alarm conditions.

The battery 234 may be operatively connected to the processor module 214(and possibly other components of the sensor electronics module 12) andprovide the necessary power for the sensor electronics module 12. Insome exemplary implementations, the battery is a Lithium ManganeseDioxide battery, however any appropriately sized and powered battery canbe used (e.g., AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium,Nickel-metal hydride, Lithium-ion, Zinc-air, Zinc-mercury oxide,Silver-zinc, or hermetically-sealed). In some exemplary implementations,the battery is rechargeable. In some exemplary implementations, aplurality of batteries can be used to power the system. In yet otherimplementations, the receiver can be transcutaneously powered via aninductive coupling, for example.

A battery charger and/or regulator 236 may be configured to receiveenergy from an internal and/or external charger. In some exemplaryimplementations, a battery regulator (or balancer) 236 regulates therecharging process by bleeding off excess charge current to allow allcells or batteries in the sensor electronics module to be fully chargedwithout overcharging other cells or batteries. In some exemplaryimplementations, the battery 234 (or batteries) is configured to becharged via an inductive and/or wireless charging pad, although anyother charging and/or power mechanism may be used as well.

One or more communication ports 238, also referred to as externalconnector(s), may be provided to allow communication with other devices,for example a PC communication (com) port can be provided to enablecommunication with systems that are separate from, or integral with, thesensor electronics module. The communication port, for example, maycomprise a serial (e.g., universal serial bus or “USB”) communicationport, allows for communicating with another computer system (e.g., PC,personal digital assistant or “PDA,” server, or the like). In someexemplary implementations, the sensor electronics module 12 is able totransmit historical data to a PC or other computing device (e.g., ananalyte processor as disclosed herein) for retrospective analysis by apatient and/or physician.

In some continuous analyte sensor systems, an on-skin portion of thesensor electronics may be simplified to minimize complexity and/or sizeof on-skin electronics, for example, providing only raw, calibrated,and/or filtered data to a display device configured to run calibrationand other algorithms required for displaying the sensor data. However,the sensor electronics module 12 may be implemented to executeprospective algorithms used to generate transformed sensor data and/ordisplayable sensor information, including, for example, algorithms that:evaluate a clinical acceptability of reference and/or sensor data,evaluate calibration data for best calibration based on inclusioncriteria, evaluate a quality of the calibration, compare estimatedanalyte values with time corresponding measured analyte values, analyzea variation of estimated analyte values, evaluate a stability of thesensor and/or sensor data, detect signal artifacts (noise), replacesignal artifacts, determine a rate of change and/or trend of the sensordata, perform dynamic and intelligent analyte value estimation, performdiagnostics on the sensor and/or sensor data, set modes of operation,evaluate the data for aberrancies, and/or the like.

Although separate data storage and program memories are shown in FIG. 2,a variety of configurations may be used as well. For example, one ormore memories may be used to provide storage space to support dataprocessing and storage requirements at sensor electronic module 12.

FIG. 3A depicts an example a diagram illustrating sensor electronicsmodule 312 in communication with multiple sensors, including a glucosesensor 320, an altimeter 322, an accelerometer 324, a temperature sensor328, and a location module 369 (e.g., a global positioning systemprocessor or other source of location information) in accordance withsome exemplary implementations. Although FIG. 3A depicts sensorelectronics module 312 in communication with specific sensors, othersensors and devices may be used as well including, for example, heartrate monitors, blood pressure monitors, pulse oximeters, caloric intake,medicament delivery devices, and the like. Moreover, one or more ofthese sensors may provide data to the analyte processing system 400and/or analyte processor 490 described further below. In someimplementations, a user may manually provide some of the data to analyteprocessing system 400 and/or analyte processor 490. For example, a usermay provide calories consumed information via a user interface toanalyte processing system 400 and/or analyte processor 490.

In the example depicted at FIG. 3A, each of the sensors 320-328communicates sensor data wirelessly to the sensor electronics module312. In some exemplary implementations, the sensor electronics module312 includes one or more of the sensors 320-328. In some exemplaryimplementations, the sensors are combined in any other configuration,such as a combined glucose/temperature sensor that transmits sensor datato the sensor electronics module 312 using common communicationcircuitry. Depending on the embodiment, fewer or additional sensors maycommunicate with the sensor electronics module 312. In some exemplaryimplementations, one or more of the sensors 320-328 is directly coupledto the sensor electronics module 312, such as via one or more electricalcommunication wires.

The sensor electronics module 312 may generate and transmit a datapackage to a device, such as display device 350, which may be anyelectronic device that is configured to receive, store, retransmit,and/or display displayable sensor data. The sensor electronics module312 may analyze the sensor data from the multiple sensors and determinewhich displayable sensor data is to be transmitted based on one or moreof many characteristics of the host, the display device 350, a user ofthe display device 350, and/or characteristics of the sensor data. Thus,the customized displayable sensor information that is transmitted to thedisplay device 350 may be displayed on the display device with minimalprocessing by the display device 350.

FIGS. 3B and 3C are perspective and side views of a sensor systemincluding a mounting unit 314 and sensor electronics module 12 attachedthereto in an embodiment, shown in its functional position, including amounting unit and a sensor electronics module matingly engaged therein.In some exemplary implementations, the mounting unit 314, also referredto as a housing or sensor pod, comprises a base 334 adapted forfastening to a host's skin. The base can be formed from a variety ofhard or soft materials, and may comprise a low profile for minimizingprotrusion of the device from the host during use. The base 334 may beformed at least partially from a flexible material, which is believed toprovide, in some implementations, numerous advantages over othertranscutaneous sensors, which, unfortunately, can suffer frommotion-related artifacts associated with the host's movement when thehost is using the device. The mounting unit 314 and/or sensorelectronics module 12 may be located over the sensor insertion site toprotect the site and/or provide a minimal footprint (utilization ofsurface area of the host's skin).

In some exemplary implementations, a detachable connection between themounting unit 314 and sensor electronics module 12 is provided, whichmay enable improved manufacturability, namely, the relativelyinexpensive mounting unit 314 can be disposed of when replacing thesensor system after its usable life, while the relatively more expensivesensor electronics module 12 can be reusable with multiple sensorsystems. In some exemplary implementations, the sensor electronicsmodule 12 is configured with signal processing, for example, configuredto filter, calibrate and/or other algorithms useful for calibrationand/or display of sensor information.

In some exemplary implementations, the contacts 338 are mounted on or ina subassembly (hereinafter referred to as a contact subassembly 336)configured to fit within the base 334 of the mounting unit 314 and ahinge 348 that allows the contact subassembly 336 to pivot between afirst position (for insertion) and a second position (for use) relativeto the mounting unit 314. The hinge may provide pivoting, articulating,and/or hinging mechanisms, such as an adhesive hinge, a sliding joint,and the like, and the action of the hinge may be implemented, in someimplementations, without a fulcrum or a fixed point about which thearticulation occurs. In some exemplary implementations, the contacts 338are formed from a conductive elastomeric material, such as a carbonblack elastomer, through which the sensor 10 extends, although thecontacts may be formed in other ways as well.

In some exemplary implementations, the mounting unit 314 is providedwith an adhesive pad 308, disposed on the mounting unit's back surfaceand including a releasable backing layer. Thus, removing the backinglayer and pressing the base portion 334 of the mounting unit onto thehost's skin adheres the mounting unit 314 to the host's skin.Additionally or alternatively, an adhesive pad can be placed over someor all of the sensor system after sensor insertion is complete to ensureadhesion, and optionally to ensure an airtight seal or watertight sealaround the wound exit-site (or sensor insertion site). Appropriateadhesive pads can be chosen and designed to stretch, elongate, conformto, and/or aerate the region (e.g., host's skin). The configurations andarrangements that provide water resistant, waterproof, and/orhermetically sealed properties may be provided with some of the mountingunit/sensor electronics module implementations described herein.

FIG. 4A depicts an example of an analyte data processing system 400, inaccordance with some exemplary implementations. The description of FIG.4A also refers to FIGS. 1 and 4B.

The analyte data processing system 400 may include one or more userinterfaces 410A-C, such as a browser, an application, and/or any othertype of user interface configured to allow accessing and/or interactingwith analyte processor 490 via, for example, network 406 and a loadbalancer 412. The analyte processor 490 may further be coupled to arepository, such as repository 475.

Analyte data processing system 400 may also receive data from sourcesystems, such as health care management systems, patient managementsystems, prescription management systems, electronic medical recordsystems, personal health record systems, and the like. This sourcesystem information may provide metadata for dynamic report generation.

Analyte data processing system 400 may be implemented in a variety ofconfigurations including stand-alone, distributed, and/or cloud-basedframeworks. However, the following description relates to animplementation of system 400 in a cloud-based framework, such as asoftware-as-a-service (SaaS) arrangement in which the analyte processor490 is hosted on computing hardware, such as servers and datarepositories maintained remotely from an entity's location (e.g., remotefrom a host, a health service provider, and like end-user) and accessedover network 406 by authorized users via a user interface, such as userinterface 410A, B, and/or C, and/or a data retriever 465.

FIG. 4B depicts a system 499 which is similar to system 400, but system499 is implemented as a SaaS-based system including a plurality ofservers 404, each of which may be virtualized to provide one or moreanalyte processors 490. Moreover, each of the virtualized analyteprocessors 490 may serve a different tenant, such as an end-user, aclinic, a host wearing a sensor, and the like. To make more efficientuse of computing resources of a software-as-a-service (SaaS) providerand to provide important performance redundancies and/or reliability, itmay, in some implementations consistent with FIG. 4B, be advantageous tohost multiple tenants (e.g., hosts, users, clinics, etc. at userinterfaces 410A-C and/or data retriever 465) on a single system 400and/or 499 that includes multiple servers and that maintains data forall of the multiple tenants in a secure manner at repository 475 whilealso providing customized solutions that are tailored to each tenant.

Referring again to FIG. 4A, in some exemplary implementations, analytedata processing system 400 may provide a cloud-based diabetes datamanagement framework that receives patient-related data from variousdevices, such as a medical device, a glucose meter, a continuous glucosemonitor, sensor system 8, display devices 14-20, source systems, and/orother devices (e.g., a device providing food consumption, such ascarbohydrates, consumed by a host or patient, medicament delivery data,time of day, temperature sensors, exercise/activity sensors, and thelike). In some exemplary implementations, the cloud-based diabetes datamanagement receives the data programmatically with little (or no)intervention on the part of a user. The data received from devices,source systems, and the like may be in a variety of formats and may bestructured or unstructured. For example, in some exemplaryimplementations, the system 400 receives raw sensor data, which has beenminimally processed or analyzed, and the received data is thenformatted, processed (e.g., analyzed), and/or stored in order to enablereport generation. For example, a data retriever 465 may be implementedat one or more devices, such as computer 20 coupled to sensor system 8.In this example, data retriever 465 formats sensor data into one or morecommon formats compatible with analyte processor 490 and provides theformatted data to analyte processor 490, so that analyte processor 490can analyze the formatted data. Although FIG. 4A depicts a single dataretriever 465, in some exemplary implementations, a plurality of dataretrievers 465 may be used to format data from a plurality of devicesand/or systems, some of which have different data formats, into asingle, common format compatible with analyte processor 490.

In some implementations, the data retriever 465 may be accessed througha kiosk including a processor, such as a dedicated computer, configuredwith a user interface, or may be accessed via a secure web-basedinterface residing on a non-dedicated computer.

In some implementations, the first time a processor (e.g., a computer, asmart phone, and any other device) accesses system 400, the dataretriever may be programatically installed on the processor bydownloading software for the data retriever to the processor's memory.The downloaded software is then programmatically installed on theprocessor, and then data retriever may generate a view (or page) whichcan be presented on a user interface to allow a user to fetch data(e.g., download data to system 400, analyte processor 490, and thelike). In some implementations, this user interface may allow a user toselect an icon, such as a fetch icon, to programmatically start a datatransfer to analyte processor 490. For example, a user selects the fetchicon at the user interface on a processor, such as computer 20, whichinitiates a data transfer from a sensor system 8 coupled to dataretriever 465 and analyte processor 490. In some implementations, thefetch icon may be implemented as a software widget. Moreover, thesoftware widget may be placed on a webpage, so that when selected afetch process begins for a registered user.

Moreover, the software associated with the data retriever may include aself-updating mechanism, so that when a fetch is selected at the userinterface, the data retriever programmatically checks for an update(e.g., software, drivers, data, and the like) at analyte processor 490(or another designated computer) and installs the update. The update maybe performed programmatically with little (or no) intervention by auser. Data downloads from a device or system to the data retriever maybe performed using a wired connection, such as a device-specificdownload cable, or wirelessly, when the device and the processor areequipped for wireless data transfer.

The analyte processor 490 may check data downloaded by the dataretriever 465 for transmission-related errors, data formatting,device-related error codes, validity of the data, duplicate data points,and/or other aspects of the data. Moreover, if out-of-range data pointsor device errors are found, analyte processor may identify those datapoints by, for example, flagging those data points, subsequentlycorrecting the identified data points programmatically or by a systemadministrator, and storing the corrected data points. Moreover, theanalyte processor may be configured by a user, such as a clinician,doctor, and the like, to perform additional data processing steps, suchas correcting time of day, correcting the date, and analyzing data byspecific cohorts, groups, and relationships (e.g., demographics, such asage, city, state, gender, ethnicity, Type I diabetes, Type II diabetes,age of diabetes diagnosis, lab results, prescription drugs being used,self-reported conditions of the patient, diagnosed conditions of thepatient, responses to questions posed to patient, and any other metadatarepresentative of the host/patient). Once analyte processor performsinitial data processing (e.g., checks, cleaning, and analysis), theprocessed data and/or the raw data provided by the data retriever may bestored at repository 475.

The processing at analyte processor 490 may also include associatingmetadata with the data received from the devices and/or sensors.Examples of metadata include patient information, keys used to encryptthe data, patient accelerometer, location data (e.g., location ofpatient or location of patient's clinic), time of day, date, type ofdevice used to generate associated sensor data and the like. The patientinformation can include the patient's age, weight, sex, home addressand/or any past health-related information, such as whether the patienthas been diagnosed as a type 1 or type 2 diabetic, high-blood pressure,or as having any other health condition. The processing may also includeanalysis, such as determining one or more descriptive measurementsand/or generating reports based on received information and descriptivemeasurements. These descriptive measurements may include statistics(e.g., median, inner and outer quartile ranges, mean, sum, n, standarddeviation, and coefficients of variation). Examples of reports aredepicted at FIGS. 6A-1, 6A-3 through 6A-28, and 7-10.

In the example of FIG. 4A, user interfaces 410A-C may be used by one ormore entities, such as end-users, hosts, health care providers, clinics,patients, research groups, health systems, medical device manufacturersand the like. These entities may remotely access analyte processingsystem 400 via user interface 410A-C to request an action, such asretrieve analyte data, provide analyte data, request analysis of analytedata, request generation of reports including modules having viewspresenting descriptive measurements of the analyte data, present analytedata and reports, and the like. For example, user interface 410A maysend a request (e.g., a message and the like) to initiate an action atan analyte processor 490, which is remote (e.g., separate from the userinterfaces and coupled by a network). The action may request a reportfor the sensor data provided by data retriever 465 (e.g., a singleprocessor, such as computer 20, may host data retriever 465 and userinterface 410A). Other examples of actions include providing sensordata, such as glucose data, carbohydrate data, insulin pump data, andthe like, to the analyte processor 490, initiating processing of thesensor data, initiating analysis of the sensor data, and storing data atrepository 475. In some exemplary implementations, the computingresources provided by analyte processor 490 may comprise one or morephysical servers virtualized to provide the analyte processing servicesdisclosed herein.

The data retriever 465 may obtain (e.g., receive, retrieve, and thelike) data from one or more sources and provide any obtained data in aformat compatible for use within analyte processor 490. In someimplementations, data retriever 465 may be implemented in one or more ofthe source systems and/or devices providing data to analyte processor490. For example, data retriever 465 may be implemented in one or moredevices, such as sensor system 8, sensor 10, display devices 14, 16, 18,and/or 20, medicament pump 2, glucose meter 4, computers/processorscoupled to those devices, and any other device capable of providing datato system 400. In these implementations, data retriever 465 receivesdata from a host device and formats the sensor data in a formatcompatible with analyte processor 490. The data retriever 465 may alsobe implemented on source systems, such as disease management systems,weight management systems, prescription management systems, electronicmedical records systems, personal health record systems, and the like.In these implementations, data retriever 465 obtains data from thesource system and formats the data in a format compatible with analyteprocessor 490.

In some exemplary implementations, data retriever 465 may, as noted, bedownloaded and/or provided automatically to a device, a computer, asystem, and the like. For example, when a user on a computer firstaccesses system 400, system 400 may automatically install and configurethe data retriever 465 on the user's computer. Once the install iscomplete, the data retriever 465 may begin fetching data for system 400and format, if needed, the data to allow processing of the fetched databy analyte processor 490. To further illustrate by way of an example,the data retriever 465 may be downloaded onto a device, such as computer20. In this example, when computer 20 receives sensor data from sensorelectronics module 12, a data retriever 465 may provide sensor dataand/or metadata in a format compatible with analyte processor 490.

In some exemplary implementations, the analyte processor 490 may processthe received data by performing one or more of the following:associating metadata with the data received from the devices, sensors,source system, and/or data retriever; determining one or moredescriptive measurements, such as statistics (e.g., median, inner andouter quartile ranges, mean, sum, n, and standard deviation); generatingreports including modules having views presenting descriptivemeasurements of the analyte data; validating and verifying the integrityof the received data from the devices, sensors, source system, and/ordata retriever; processing received data based on metadata (e.g., toselect certain patients, devices, conditions, diabetic type, and thelike), and/or correlating received data from the devices, sensors,source system, and/or data retriever, so that the data can be comparedand combined for processing including analysis.

Moreover, the results of any processing performed by analyte processor490 may be used to generate one or more reports including selectedmodules having views presenting descriptive measurements presented asgraphs, bar graphs, static charts, charts, and the like. Furthermore,the reports and other outputs generated by system 400 may be providedvia one or more delivery mechanisms, such as report delivery module 420K(e.g., as email, secure email, print, text, presentations for display ata user interface, such as at user interface 410A-C hosted at a tablet orother processor), machine-to-machine communications (e.g., via thirdparty interface 420J), and any other communication mechanism.

In some exemplary implementations, the reports may be customizeddynamically for use by an entity, such as a host, an end-user, aclinician, a healthcare provider, device manufacturer and the like.Furthermore, the reports may be customized based on the types and/orquantity of sensors and systems providing data to system 400 and thetypes of metadata available to system 400. This customization may beperformed by a user, by system 400 programmatically, or a combination ofboth.

In some exemplary implementations, one or more of the user interfaces410A-C may be implemented on a processor, such as computer 20 or otherprocessor, serving a kiosk at a health care provider, clinic, and thelike. For example, a user, such as a host (also referred to as apatient), may enter a health care facility and access the kiosk in orderto couple and to provide sensor data and/or metadata to system 400. Inthis example, the user may provide sensor data and/or metadata to system400 and then view at one or more of user interfaces 410A-C one or morereports including information representative of the sensor data and/ormetadata provided to system 400 including statistical measures of thedata. Although the previous example using the kiosk, the kiosk may alsobe used by a health care provider or administrative staff as well.

Although the previous example refers to computer 20 including a userinterface and a data retriever to provide a kiosk, the user interfacemay be located in other devices, such as smart phones, tablet computers,display devices, and other like processors. Moreover, the computer 20may be located at locations other than a kiosk. For example, computer 20may be located at a host's home and include a data retriever 465 toretrieve data from a sensor associated with the host, so that the dataretriever 465 can format and provide the sensor data to analyteprocessor 490. The user interface and data retriever may also beconfigured at a workstation of a health care provider or clinician.

Analyte processor 490 may include, in some exemplary implementations, anauthenticator/authorizer 420A for authorizing access to analyteprocessor 490, a data parser 420B for parsing requests sent to analyteprocessor 490, a calculation engine 420H for receiving data from sensorsand processing the received data into counts for use with histograms,logic 420C, a data filter 420D, a data formatter 420E, a reportgenerator 420G, a pattern detector 420I, a report delivery module 420Kfor delivering reports in a format for the destination, and a thirdparty access application programming interface to allow other systemsand device to access and interact with analyte processor 490.

Analyte processor 490 may receive a request from a user interface, suchas user interface 410A-C, to perform an action (e.g., provide data,store data, analyze/process data, request a report, and the like).Before analyte processor 490 services the request, the analyte processor490 may process the request to determine whether the request isauthorized and authenticated. For example, authenticator and authorizer420A may determine whether the sender of the request is authorized byrequiring a user to provide a security credential (e.g., a useridentifier, a password, a stored security token, and/or a verificationidentifier provided by text message, phone, or email) at a userinterface presented on a computer. If authorized, authenticator andauthorizer 420A may authenticate the sender of the request to checkwhether a security credential associated with sender of the requestindicates that the sender (e.g., a user at user interface 410A) isindeed permitted to access a specific resource at system 400 in order toperform the action, such as store (or upload) data at repository 475,perform analyze/process data, request report generation, and the like.

To further illustrate, the data retriever 465 associated with a sensorsystem 8 and a computer 20 may be authorized and authenticated byauthenticator and authorizer 420A to access analyte processor 490 inorder to write data to a buffer or other storage mechanism, such asrepository 475. On the other hand, an entity, such as a user, at userinterface 410A may be authorized and then authenticated by authenticatorand authorizer 420A to access analyte processor 490, but only permittedto access certain information. In this second example, the user at userinterface 410A may be authorized and authenticated to access repository475 to view certain information corresponding to the user's own glucosedata and access reports generated for the glucose data, but the userwill not be authorized and authenticated to access another user's dataand/or reports. Another example may be the case when a user associatedwith a clinic, a hospital, and/or a research group requests access toall data associated with their patients. In this example, the user maybe granted access to their patients but not other patients. Yet anotherexample may be the case when a user associated with a clinic, ahospital, and/or a research group requests access to all data associatedwith patients using a certain device, such as a specific type analytesensor. In this example, the user may be granted access to data specificto the type of analyte sensor but not other sensors (and PII may beremoved or made anonymous).

Once authorized and/or authenticated, the request received at analyteprocessor 490 may then be parsed by data parser 420B to separate anydata, such as sensor data, metadata, and the like, from the request. Insome implementations, data parser 420B may perform check dataformatting, device-related error codes, validity of the data, duplicatedata points, and/or other aspects of the data. Moreover, the data parser420B may associate additional metadata with the separated data. Themetadata may include any of the metadata described herein, including anowner of the data, a key to track the data, an encryption key unique toeach user, time of day, date information, one or more locations wherethe data is (or will be stored), and the like. In some exemplaryimplementations, the data parsing 420 may provide data to thecalculation engine 420H for formatting the data into counts andhistograms as described further below.

In some exemplary implementations, the request (or the parsed datatherein) may be processed by calculation engine 420H. The calculationengine 420H preprocesses the data received from devices, sensors, andthe like to form “counts.” The counts represent a measured value, suchas an analyte value measured by a sensor, a glucose value measured by asensor, a continuous glucose value measured by a sensor, and/or otherdiabetes related information, such as carbohydrates consumed,temperature, physical activity level, and the like, and how often thatmeasured value occurred.

FIG. 5 depicts an example implementation of the calculation engine 420H.When a request 502 is received at the calculation engine 420H, thecalculation engine 420H may preprocess 504 the request to extract data,such as sensor data, and thus form a count. The count may represent anumerical value representative of sensor data provided by, for example,data retriever 465, computer 20, sensor system 8, and/or any othersource of data and how often it occurred. For example, the count mayrepresent a glucose value measured by a continuous glucose sensor, suchas continuous analyte sensor 10, and how often it occurred over acertain period of time.

The calculation engine 420H may also preprocess 504 the request 502 toprovide and/or determine other metadata, such as to determine personalidentifiable information (PII) 506 associated with the request 502, timeof day/date, and the like, although in some implementations thecalculation engine may receive the request without PII information. ThePII may include a serial number of the sensor system 8 and any otherinformation that may identify the host associated with sensor system 8.In some exemplary implementations, the PII may be stored in repository475 in an encrypted form to enhance the privacy of the PII. Furthermore,the PII may be encrypted with an encryption technique and/or key that isdifferent from other information stored at repository 475. For example,analyte processor 490 may store at repository 475 data from a pluralityof users, such as hosts, patients, and the like. To maintain privacy,each user's data may be encrypted with a separate key. Moreover, PIIinformation may be encrypted with yet another key to further enhance theprivacy of the user.

The calculation engine 420H may then use the count 508 to performadditional processing. The additional processing may include storing thecount in repository 475, which may include one or more databases tostore the counts. Moreover, the count may be stored with metadata, suchas time of day/date information, the original request 502, and the like.Furthermore, the count may be encrypted, as noted, before storage inrepository 475, and, in some exemplary implementations, the count and/ormetadata may be encrypted with an encryption technique and/or key thatis different from the PII.

Although some of the examples described herein refer to databases, thedatabases may also be implemented as any type of data store, such asrelational databases, non-relational databases, file systems, and thelike.

The calculation engine 420H may also use the count to update one or morehistograms 510. For example, rather than keep track of and process ahost's glucose levels over a certain period of time using raw sensordata values, the calculation engine 420H may convert the data valuesinto counts. The counts may be added to a histogram 590A for a givenhost. In the example of FIG. 5, the histogram 590A includes an x-axis ofglucose concentration values and a y-axis of the number of occurrencesfor each glucose concentration value. In this example, if the count 508for a host is 60, the calculation engine 420H updates 512 the binassociated with the value 60. The histogram 590A may be associated witha given patient/host to represent the glucose levels of the host.Because the possible glucose concentration levels typically fall into acertain range, the values of the bins can be predetermined in someexemplary implementations.

In some exemplary implementations, the histogram 590A may also beassociated with a given time during the day (also referred to as anepoch). For example, histogram 590A may represent a time, such as 1 PMto 1:30 PM, and, in this example, calculation engine 420H may generateother histograms for other times.

In some exemplary implementations, the calculation engine 420H maygenerate a plurality of histograms for a given host for given timeperiods. For example, 48 histograms corresponding to 30-minute epochsover a 24 hour period may be generated, so that each time a count isreceived, the count is added to one of the 48 histograms based on a timeassociated with the count and a corresponding histogram. In thisexample, a count representative of a blood glucose measurement made at12:30 AM would update a histogram designated to cover measurements madeduring that epoch, while another count with a time of day at 1:30 AMwould update another histogram assigned to represent the 1:30 AM epoch.Moreover, these 48 histograms may be stored in a database in a datastructure to facilitate access. For example, each of the 48 histogramsmay be stored as rows in a database. Moreover, calculation engine maydetermine, based on the one or more histograms, statistics using settheory as described further below.

Although the previous example utilizes a 30-minute interval as an epoch,other intervals of time, such as 15 minutes, and the like, may be usedas well.

In some exemplary implementations, the calculation engine 420H may alsoupdate other histograms representative of aggregate count information.For example, the count 508 may be used to update histograms 510representative of so-called “cohorts” of the host used to generatehistogram 590A. The term “cohorts” refers to hosts that can be grouped,and this grouping may be based on one or more factors, such as ademographic, a health condition, an age, a geographic location (e.g.,country, state or zip code), and the like. In the example of FIG. 5, thehistogram 590B is updated 514 with the same count value as histogram590A, but the histogram 590B represents cohorts associated with, forexample, all of the other patients in a clinic where the host is beingtreated. As such, histogram 590B may provide insight into the host andthe host's cohorts at the clinic.

Moreover, the calculation engine 420H may also update other histogramsused to pre-calculate statistics associated with the host or cohorts.For example, the calculation engine may update histograms (which areassociated with a certain patient) and update other histograms (whichmay be for other patients, such as cohorts selected based on metadata,such as a zip code, an age, a gender, and the like). In addition, thecalculation engine 420H may also form other histograms based onstatistics, such as a union, an intersection, a set difference, and thelike. For example, calculation engine 420H may use set theory todetermine the union of histograms. The union represents the set of allobjects that are in a first histogram A, a second histogram B, or acombination of both (denoted A ∩B). The calculation engine 420H may alsodetermine intersections (e.g., the set of all objects that are only infirst histogram A and the second histogram B, denoted A U B), and maydetermine the set difference (e.g., the set of all members of the firsthistogram A that are not in second histogram B, denoted A\B).

In some implementations, the calculation engine uses the histograms andset theory operations to determine aggregate statistical information andto form a so-called aggregate histogram. For example, a report may begenerated to include an aggregate histogram for all patients in ageographic region, such as the United States. In this example, thecalculation engine may identify existing histogram groups that providethe smallest number of histograms to cover the geographic region ofinterest. Specifically, the histograms of all patients (or clients) thatare in the United States may be merged using set theory to form avirtual histogram of the United States for a given time frame, such asthe past 30 days. In addition, this operation may, in someimplementations, be performed very rapidly, when compared to performingsuch operations on raw sensor data. In some implementations, therepository may store a plurality of histograms (e.g., histograms may beorganized based on patient, clinic, zip code, etc.) which can be readilyprocessed using set theory to form the aggregate histogram or determinestatistics for the aggregate histogram. Moreover, in someimplementations, an aggregate histogram may be configured for storage atthe repository, in which case the calculation would update the aggregatehistogram with counts rather than generate it using set theory. Althoughthe previous example refers to the aggregate histogram generated basedon geographic location, the aggregate histogram may be generated onother metadata described herein as well (e.g., demographics, age, zipcode, type of diabetes, age of diagnosis, and the like).

In some implementations, the calculation engine 420H may have to update,as noted, a plurality of histograms. When this is the case, thecalculation engine 420H may update the histograms in a distributedmanner based on eventual consistency.

Although the description with respect to the calculation engine 420Hrefers to a histogram, the histogram, as used herein, refers to a datastructure that includes one or more values (e.g., values) associatedwith one or more time intervals. For example, the histogram mayrepresent one or more values, such as frequency of occurrence,associated with bins corresponding to one or more time intervals.Moreover, this data structure may be stored at a database, so that it isreadily accessed with a read, such as in a row of a database (or, forexample, in a column if a column database is used).

In some exemplary implementations, repository 475 stores the histogramsincluding counts in a database. For example, repository 475 may storedata for a patient that covers a time frame, such as 1 day, 2 days 7days, 30 days, or more. In this example, the days may be subdivided intoepochs, each of which has a corresponding histogram stored in repository475. Moreover, each histogram may be stored as a row (or column) in adatabase at repository 475 to facilitate fast data access.

Referring again to FIG. 4A, logic 420C may also process requests toperform an action (e.g., store, retrieve, process, analyze, report data,and the like) at analyte processor 490. For example, logic 420C maycontrol the actions of the analyte processor 490 when processing arequest to store data at repository 475. In this example, the requestmay, under control of logic 420C, be parsed at data parser 420B,converted into a count at calculation engine 420H, added to histogram590A-590B, and then forwarded to repository 475 for storage. Moreover,this process may occur serially and/or asynchronously (e.g., the dataparser may extract data and provide data for asynchronous updating ofcounters associated with histograms, and the subsequent data store atthe repository may occur asynchronously or substantiallysimultaneously).

Logic 420C may also determine one or more descriptive measurements, suchas statistics (e.g., a median, inner and outer quartile ranges, a mean,a sum, a standard deviation, and the like) based on counts, histograms,and/or received sensor data. The logic 420C may provide thesedescriptive measurements to the report generator 420G to enable reportgeneration (e.g., for presentation at a user interfaces 410A-C). Forexample, the mean may be determined by summing the product of the countand the bin value and then dividing that sum by the sum of the counts.Referring again to FIG. 5 at histogram 590A, the mean is 46(20*1+30*2+60*4)/(1+2+6).

The pattern detector 420I may perform pattern detection on data, such assensor data representative of blood glucose data, analytes, and otherdata as well (e.g., insulin pump data, carbohydrate consumption data,and the like) processed by analyte processor 490 and stored atrepository 475. Moreover, the pattern detector 420I may detect patternsretrospectively for a predetermined time period defined by system 400and/or a user.

In some exemplary implementations, the pattern detector 420I may receiveinput data from the repository 475, and the input data may includesensor data representative of glucose concentration data, analytes, andother data as well (e.g., insulin pump data, carbohydrate consumptiondata, histograms and/or counts, data from a continuous glucose monitor(CGM data), time of day, amount of carbohydrates, other food relatedinformation, exercise, awake/sleep timer intervals, medicationsingested, and the like). Moreover, the input data may comprisehistorical data obtained over a time frame, such as 8 hours, 1 day, 2days, 7 days, 30 days, and/or any other time period. For example, theinput data may comprise counts representative of monitored analytedetection levels (e.g., glucose concentration levels) received andstored at system 400 over a period covering a four-week time frame.

The pattern detector 420I may analyze the input data for patterns. Forexample, patterns can be recognized based on one or more predefinedrules (also referred to as criteria or triggers). Furthermore, the oneor more predefined rules may be variable and adjustable based userinput. For example, some types of patterns and rules defining patternscan be selected, turned off and on, and/or modified by a user, a user'sphysician, or a user's guardian, although system 400 may select, adjust,and/or otherwise modify rules programmatically as well.

Some examples of the types of relationships in the input data that canbe considered a pattern are one or more of the following: a glucoselevel that exceeds a target glucose range (which may be defined by auser, a health care provider, system 400, or a combination thereof); aglucose level that is below a target glucose range; a rapid change inglucose level from a low to a high (or vice versa); times of day when alow, a high, an at range, or rapid glucose level event occurs; and/ordays when a low, a high, an at range, or a rapid glucose level eventoccurs.

Additional examples of the types of relationships in the input data thatcan be considered a pattern include hypoglycemic events by time of day.As an example, a pattern may be identified in situations where the userhas low glucose concentrations around the same time in the day. Anothertype of pattern, which may be identified, is a “rebound high” situation.For example, a rebound high may be defined as a situation where a userovercorrects a hypoglycemic event by overly increasing glucose intake,thereby going into a hyperglycemic event. These events may be detectedbased on one or more predefined rules. Patterns that may be detectedinclude a hyperglycemic pattern, a hypoglycemic pattern, patternsassociated with a time of day or week, a weighted scoring for differentpatterns based on frequency, a sequence, and a severity. Patterns mayalso be based on a custom sensitivity of a user, a transition from ahypoglycemic to hyperglycemic pattern, an amount of time spent in asevere event, and a combination of glucose change and time information.Detected patterns may also be patterns of high variability of glucosedata. Further, a pattern may be based on a combination of previouspattern data and a currently detected situation, whereby the combinedinformation generates a predictive alert.

The pattern detector 420I may detect the pattern and generate an output,which may be provided to report generator 420G for reporting. Moreover,the report may include a retrospective analysis of the input data andany patterns determined by pattern detector 420I. Although the previousexample describes an approach for detecting patterns in data, otherapproaches may be used as well.

The data filter 420D may be used to check whether an output generated byanalyte processor 490, such as a response for certain types of data, areport, and the like, does not violate a data rule. For example, thedata filter 420D may include a data rule to check whether a responseincludes data, such as PII, to a destination which is not authorized orallowed to receive the response (e.g., based upon authorization andauthentication and the corresponding role of the user making therequest).

The data formatter 420E may format data for delivery based on the typeof destination. For example, the data formatter 420E may format a reportbased on whether it is being sent to a printer, a user interface, asecure email, another processor, and the like.

The report generator 420G may generate one or more reports. The reportsmay provide descriptive information, such as statistical information,representative of the sensor data received at analyte processor 490.Moreover, the report may provide a retrospective analysis of the sensordata stored at repository 475. For example, the report may providestatistical information based on sensor data (and/or correspondinghistograms including counts) over a time frame, such as 8 hours, 1 day,2 days, 7 days, 30 days, and any other time frame. Moreover, the reportmay allow a user, such as a patient, a host, or a clinician, to view thereport and identify trends and other health related issues.

In some exemplary implementations, report generator 420G generatesreports based on data received and/or stored at system 400 (e.g., usingsensor data, metadata, counts, histograms, and the like). Examples ofreports and/or the modules that may be used in a report are depicted atFIGS. 6A-1, 6A-3 through 6A-28, and FIGS. 7-11.

Moreover, the report generator 420G may configure reports based on themetadata representative of the types of sensors providing sensor data tosystem 400, the quantity of sensors providing sensor data to system 400,a user preference, such as a selection by a host and/or a clinician, asize of the display of a user interface, and/or the length of thereport.

In some exemplary implementations, the report represents retrospectivedata (also referred to as historical data) received and stored at system400 over a certain time frame, such as 8 hours, 1 day, 2 days, 7, days,30 days, since the last upload of data to system 400, since the lastvisit to doctor/clinic, and any other time frame. For example, a userand/or a clinician may access user interface 410A and select a timeframe over which the data should be retrieved from repository 475 foranalysis (e.g., retrieve glucose data, carbohydrate consumption data,and insulin pump data measured for a host in the past 30-days and/orhistograms including counts representative of such measured data).Although the previous example describes user selection of the timeframe, the time frame may be programmatically selected by system 400 aswell. In any case, the report generator 420G may compile a report usingone or more modules described further below.

FIG. 6A-1 depicts an example of a report 700, which may be generated byreport generator 420G. The description of FIG. 6A-1 also refers to FIG.4 a.

The report 700 may include one or more modules, 710A-D. The modules710A-D may be self-contained, in the sense that they can be usedindependently of each other. For example, the report 700 can include oneor more modules 710A-D, and the modules 710A-D may be placed in avariety of positions within the report. Moreover, the modules 710A-D inthe report 700 may be dynamic in the sense the specific types of modulesselected for a report may vary based on metadata. The metadata mayinclude one or more factors, such as types of data available during thereported timeframe (e.g., sensor data and metadata), amount of dataavailable during a timeframe, devices being used (e.g., insulin pump,glucagon pump, single-point glucose meter, continuous glucose meter andthe like), user preferences (e.g., preference of a patient, a doctor, aclinician, and the like), size of the user interface available topresent report 700, patient demographics, pre-selected/configuredpreferences provided by a user and/or system 400, other modules beingused in the report (e.g., a certain module may not be allowed to be usedwith another module, while it may be required to use another module withthe certain module), a quantity of information to be displayed (e.g., acontinuous glucose monitor generating relatively more data than aself-monitoring blood glucose device may require certain modules),and/or any other factor.

In some exemplary implementations, a request may be received at, forexample, report generator 420G. When the request is received, the reportgenerator 420G may generate report 700 based on metadata as noted above.For example, metadata may be accessed by the report generator 402G toobtain information related to one or more factors. For example, themetadata may include patient information including report preferences,types and quantity of devices used, and display size being used topresent the report, and other data related to the user, devices, and thelike as noted above. The metadata may also include rules, such aswhether a module can be used with certain devices (for example, certainreports may only be suitable for continuous blood glucose, rather thandiscrete measurements), whether a module can be used with certainpatient conditions (for example, a caregiver may establish a rulerequiring a certain report based on a patient's demographic, history, ageneral condition, and/or a condition or state at any instant of time),whether a module may be used on certain display sizes, whether a modulemay be used given a certain volume of data or device type, and/or anyother rules defining which modules can be used in a given report.

In some example embodiments, report generator 420G may access metadataincluding templates. For example, a template may define the placement ofone or more modules in a report.

The framework defining the placement of each module 710A-D may be atemplate (also referred to as a model). Moreover, templates may bedefined for certain devices or displays, so that when the request ismade and/or metadata obtained, the report generator 420G can dynamicallyselect, based on metadata, one or more modules into the predefinedtemplate. For example, a certain display device may be of a size thatallows four modules to be displayed as depicted at FIG. 6A-1, whileanother display device may be of a size that allows two modules, and soforth. Although FIG. 6A-1 depicts an example implementation includingfour modules, other quantities (as well as placement of those modules)may be used as well.

In some exemplary implementations, the metadata may include a pluralityof predefined templates configured for a specific patient, a specificcaregiver, a specific medical professional, a group of patients (e.g.,cohorts), a businessperson, and/or the like. As such, modules may bedynamically selected based on an evaluation of the metadata. Moreover,the use of the templates may, in some implementations, allow the dynamicgeneration of modules to be performed more rapidly, when compared to notusing the templates. In any case, when the report generator 420G selectswhich modules 710A-D are to be included in report 700, the reportgenerator 420G may then obtain the underlying data (for example, sensordata, demographics, and the like) to be used in the selected modules.

FIG. 6A-2 depicts an example process for generating dynamic reports inaccordance with some exemplary implementations. The description of FIG.6A-2 also refers to FIG. 4A.

At 715, a request may be received to generate a report. For example, thereport generator 420G may receive from a processor 20, a device 18, 16,or 14, and/or any other user interface, a request to generate a reportR00 including one or more modules 710A-D. This request may includeinformation, such as the identity of the patient, identity of therequesting device, a type of report being requested, and/or the like.The request may also specify a time frame for the report and/or as anyother information required to authenticate the device requesting deviceor user.

At 720, one or more modules, such as the report modules disclosedherein, may be selected based on metadata including rules, templates,and/or the like. This metadata may describe one or more of thefollowing: types of data available; amount of data; types of devicesbeing used; user preferences; size of the user interface available topresent report; patient demographics; patient information includingreport preferences, types and quantity of devices used, and display sizebeing used to present the report, and other data related to the user,devices, and the like; rules, such as whether a module can be used withcertain devices (for example, certain reports may only be suitable forcontinuous blood glucose, rather than discrete measurements), whether amodule can be used with certain patient conditions (for example, acaregiver may establish a rule requiring a certain report based on apatient's demographic, history, or condition), whether a module may beused on certain display sizes, whether a module may be used given acertain volume of data or device type; and/or one or more templates. Forexample, the selection of modules may be performed based on metadataincluding user preferences for certain modules, a type of device beingused, a display area of the device, and a rule defining which modulescan be used given the type of device, a patient state/condition, and thedisplay area of the device. Furthermore, the metadata may be stored at arepository, such as repository 475, although some of the metadata or maybe provided as part of the request received at 710.

At 725, the report may be generated based on the modules selected at720. For example, if the metadata indicates that the user prefers twospecific modules (e.g., a first module for a continuous glucose monitorand a second module for a self-monitoring glucose monitor) and themetadata indicates that current device being used is a continuousglucose monitor, the report generator 420G may dynamically select thefirst module. However, if a second request is received but the metadataindicates that a self-monitoring glucose monitor is being used, thereport generator 420G may dynamically select the second module.Moreover, the module(s) may be positioned in the report based on thepredefined templates noted herein.

In some implementations, the analyte processor 490 may include one ormore defaults for the report 700 including the modules therein.Moreover, the defaults may be dynamic, in the sense that the defaultsvary based on the patient. For example, if a host has Type 1 diabetes,the default target glucose range may be defined as 70-180 mg/dL, and forType 2 diabetes, the default target range may be defined as 90-130mg/dL, although these defaults may be changed by a user, such as aclinician, a doctor, a patient, and the like. Moreover, the analyteprocessor 490 may base the report and/or certain modules on a defaulttime frame of data, such as the most recent 30 days of data, althoughother default time frames may be used as well. Furthermore, if there isa gap in the data provided to the analyte processor that prevents 30continuous days of analysis, the report may start at the most recentdata and go back as far back as possible without exceeding the 30-daylimit.

FIG. 6A-3 depicts an example of a patient information module 605A. Thepatient information module 605A may provide information to identify apatient, such as a patient's name 605B, a date of a medical appointment605C, an email address 605D, a condition 605E, and any other informationthat may be used to identify a user, such as a patient, a host, medicalrecord number, and the like. In some exemplary implementations, patientinformation module 605A may be configured at a top portion of report 600to enable quick identification of the patient. Although patientinformation module 605A depicts personally identifiable information(PII), such as name 605B and email address 605B, patient informationmodule 605A may be configured anonymously to avoid disclosure of the PIIinformation.

FIG. 6A-4 depicts an example of a highlights module 607A. The highlightsmodule 607A may include one or more sub-modules 607B-E providing anabstraction of the data received and processed at analyte processor 490.In some exemplary implementations, the highlights module 607A providesan abstraction by distilling the details of data obtained over a timeframe, such as 8 hours, 1 day, 2 days, 7 days, 30 days, and/or any othertime period, into a graphical representation with some textualinformation. In the example of FIG. 6A, the time frame of the data isdepicted at 607F (which in this example is about a month although othertime frames may be used as well). The glucose module 607B may abstract30 days of data provided by a continuous blood glucose sensor (which mayrepresent large volumes of data likely to inundate a patient orclinician). This data may be abstracted into, for example, a graphicalelement, such as graphical bar 609A, and textual information, such ascallouts 609D-F, to represent the percentage of time over the past30-days that the patient was below, above, or at a target range.

In some exemplary implementations, the abstraction may convey complexstatistical information in a graphical bar format. Furthermore, thegraphical bar may represent different values, states, or conditionsusing different shading, and may include callouts including textualinformation to provide summary information, value help, and the like.The different shading may also be used to convey different states of ahost or patient determined based on received data (e.g., counts, etc.).For example, the glucose module 607B may convey a target glucose rangeusing the lightest shading 609A on the graphical bar 609B, while othershades may be used to represent other portions of the glucose range of ahost. Although some of the examples described herein refer to usingshading, other distinctive graphical elements may be used as well, suchas colors, icons, or other elements.

In some implementations, each sub-module of the highlights module 607Auses a horizontal bar with different shading to denote ideal and lessthan ideal values. For example, the lighter the shade the more ideal thevalue. Moreover, the sub-modules may be different based on whetherself-monitoring glucose concentration data values or continuous glucosemonitor data values are being presented. For example, the title anddescriptive text may indicate whether the module is continuous data ornot, and in some instances, a sub-module may not be relevant if there isonly non-continuous glucose data values (e.g., obtained from a fingerstick meter) or only continuous glucose monitor data values. The glucosemodule 607B may include a textual legend 609C describing a target rangefor glucose measurements, the graphical bar 609B spanning a range ofglucose measurement values and including one or more different shades(at least one shade 609A distinguishable from the other shades to enablerepresentation of a target range), and callouts 609D-F.

In the example depicted at glucose module 607B, the shadingcorresponding to 180-315 denotes the range from the maximum targetglucose range times a certain factor (e.g., 1.75), and the labels 25%and 75% denote the inner and outer quartiles which are distinctivelyshaded, although other shading schemes, ranges, and factors may be usedas well.

The glucose module 607B may present statistical information determinedfor a host, such as a patient identified at 605B, over a time framedefined at report generator 420G by, for example, a user (e.g., apatient, a clinician, and/or programmatically selected by system 400).In the example of FIG. 6A, the time frame of the statistics is over aprevious 30 days, although other time frames, such as an 8 hour period,daily, weekly, monthly, yearly, and the like, may be used as well.Moreover, the statistical information may be determined from datareceived at analyte process 490 for one or more devices, source systems,and the like. For example, the data may represent sensor data (e.g.,continuous blood glucose data, insulin pump data, self-monitoring bloodglucose data, carbohydrate consumption data, and the like) and/ormetadata provided to analyte processor 490 and, in some exemplaryimplementations, be formatted in accordance with counts, although otherdata formats may be used as well.

In some exemplary implementations, the statistical information presentedat glucose module 607A may be determined using 30 days of data stored atrepository 475 in one or more histograms including counts, althoughother data formats may be used as well, including any other data formatdiscussed herein. Although the previous example describes 30 days ofstored data, other time frames may be used as well and the time framesmay be selected by a user as well. Moreover, the graphical bar 609B mayinclude a callout 609D including textual information indicating thatover about the last thirty days 607F 25% of the glucose measurementsreceived at analyte processor 490 from one or more devices were in thetarget range, and the callout 609D includes textual information showingthat the inner quartile was 123. The callout 609E shows that the medianover about the past 30 days was 163, and the callout 609E shows theouter quartile was 205.

FIG. 6A-5 depicts examples of glucose modules and, in particular,glucose distribution modules 750 and 760. The glucose distributionmodules 750 and 760 each provide a graphical abstraction of the sensordata provided to analyte processor 490, which in the depicted examplesrepresent glucose values. Glucose values within a target range aredepicted with a graphically distinct indication, such as first, lightshading, while values outside the glucose range are depicted withanother graphically distinct indication, such as second darker shading.Textual information may be presented adjacent to the graphicalabstraction. This textual information may include statisticalinformation determined from the sensor data. For example, module 750depicts a mean value of 171 for the given time frame, with a standarddeviation of plus or minus 64, a median (e.g., 123) including quartilevalues (e.g., 163 and 206), an indication of the variation of theglucose values, such as a coefficient of variation (CV), which in thisexample is 37%, a glucose range including percentages and graphicalindicators, a minimum glucose value (e.g., 39), and a maximum glucosevalue (e.g., 401). In the example of FIG. 6A-5, the arrows associatedwith the glucose range indicate above, at, or below the glucose range.Specifically, a host/patient may have a range of glucose values, andsome of the measured glucose values may be below (e.g., 4%, which isrepresented by a down arrow), some may be within range (e.g., 58%, whichis represented by a horizontal arrow), and some may be above the range(e.g., 39%, (which is depicted with a vertical up arrow). Module 760depicts the textual information as well but in a different format. Inany case, modules 750 and 760 provide a single view compressing over amonth's worth of data into a graphical abstraction highlighting thefrequency of occurrence of within range measurements and out of rangemeasurements.

Referring again to FIG. 6A-4, the stability module 607C may include atextual legend 609G describing that stability measures glucosevariability and its rate of change and that higher values of stabilitymay be considered better for a patient. The stability module 607C mayalso include a graphical element, such as graphical bar 609H, presentinga range of glucose variability values using different shades and acallout 609I including textual information describing whether glucosevariability over a certain time frame (which in this example is about 30days 607F) is very low, low, moderate, or high.

In some exemplary implementations, the stability module 607C may presenta metric that combines glucose variability (which may be determined as acoefficient of variation) and a quantity of instances and time spentwithin a state of rapid glucose change (acceleration). For example,analyte processor 490 may receive sensor data from data retriever 465,which obtains the data from a sensor, such as a sensor system 8. In thisexample, the glucose variability and the acceleration may be assigned ascore with a range, such as 0 to 50, and a lower score may be consideredbetter than a higher score. The variability score may be determined bynormalizing the coefficient of variation for a certain time frame (e.g.,30 days and the like) to the score range of 0 to 50, where a coefficientof variation of 0.7 or higher receives a maximum score of 50. Theacceleration score may be determined by sampling the rate of change ofthe sensor data stored in repository 475 as histograms and counts over awindow (e.g., over a 15-minute window/interval) by evaluating the rateof change over the sampling window, measured as an estimated mg/dL/min(milligrams/deciliter/minute) based on the rate of change of the currentand previous sampling windows. The combined variability and accelerationscore, which is a weighted mg/dL/min, may be calculated over some, ifnot all of, the sampling windows for the time frame of the report ormodule 607C, and then normalized to a scale from 0 to 50, for example.In this example, a weighted equivalent of 6 mg/di/min or higher mayreceive a score of 50. The variability and acceleration scores may thenbe combined (e.g., added) to determine the location of the callout 609Ion the graphical bar 609H.

In some exemplary implementations, the stability module 607C may only beincluded in report 600 when sensor data from a continuous glucosemonitor is available for processing.

Moreover, although the previous example describes that the stabilitymodule 607C present a metric that combines coefficient of variation andacceleration, stability module 607C may be configured to provide ametric representative of one of the coefficient of variation or theacceleration as well.

Although the previous example describes specific numerical values, unitsof measure, and the like, these values and units of measure are onlyexemplary as other values may be used as well.

The time between module 607D may include a textual legend 609Jindicating that a target range for glucose measurements is between twovalues (e.g., 70 and 180 mg/dL) and that a higher percentage of timespent within the target range is typically better for a patient. Thetime between module 607D may also include a graphical element, such as agraphical bar 609O, presenting a percentage range visually usingdifferent shades, and a callout 609L including textual informationrepresenting the percentage of time that the glucose measurements arewithin the target range. In some implementations, the time betweenmodule 607D is generated when continuous glucose sensor data isavailable from a continuous glucose monitor. But when onlynon-continuous glucose data is available (e.g., only data available froma self-monitoring blood glucose monitor), the time between module 607Dmay instead be configured to represent time between tests performedusing the self-monitoring blood glucose monitor or the percentage ofself-monitoring blood glucose tests between the range.

The time below module 607E may include a textual legend 609K describingthat the percentage of time spent below a certain value, such as 70mg/dL, is typically better for a patient. The time below module 607E mayalso include a graphical element, such as graphical bar 609N, presentinga percentage range using one or more different shades, and a callout609M including textual information representing the percentage of timethat the glucose measurements are less than the certain value, which inthis example is 70 mg/dL. In some exemplary implementations, the timebelow module 607E is generated for continuous glucose monitor data, butwhen only self-monitoring blood glucose monitor data is processed, thetime below module 607E may instead be configured to represent timebetween tests performed at the self-monitoring blood glucose monitor.Although module 607E is depicted as a time (or tests) below presenting apercentage, the module 607E may be configured as a time (or tests) abovemodule presenting a percentage of time (or tests) that glucose valuesare above a certain value.

FIG. 6A-6 depicts an example of a percentage tests above module 674PS,which is similar to the time below module 607E but shows the percentageof time above the target range.

FIG. 6A-7 depicts an example of an insights module 615 configured toprovide an abstraction of one or more patterns detected based on apatient's data processed by analyte processor 490. In some exemplaryimplementations, an insight includes a graphical element 617A, such asan icon, and textual information 617B. For example, insight 617C mayrepresent one or more patterns detected by analyte processor 490 (and insome exemplary implementations pattern detector 420I) over a certaintime frame, such as over an 8 hour period, 1 day, 2 days, 7, days, 30day period, and the like. Furthermore, the patterns may be so-called“hard coded” to trigger once an event is detected in the data. Forexample, a pattern may detect high glucose concentration levels above atarget glucose range and then correlate those with days of a week andtimes of day to determine whether a patient experiences high bloodglucose during a specific day or time. In this example, a detectedpattern may correspond to detecting that a patient has a high glucoseconcentration level every evening after 8 PM or has a high glucoseconcentration level on Sunday evenings for the past 30-days.Furthermore, pattern detector 420I may, in some exemplaryimplementations, detect the pattern based on the histograms and countscovering the past 30-day period. Other patterns may be detected, basedon data from the sensors and/or metadata, and presented as well byinsights module 615. For example, metadata may indicate the patient hasType II diabetes and using certain devices, such as a self-monitoringblood glucose monitor and an insulin pump. In this example, the metadatamay be used to determine what patterns to use for detection and whatinsights (and how many) to present at the insights module 615.

In some exemplary implementations, the insights presented at insightsmodule 615 may be weighted to emphasize certain insights or events andthen ranked to enable presentation of some, rather than all, of theinsights, which may be detected by analyte processor 490 and/or patterndetector 420I. For example, the insights module 615 may be configured toinclude more (or fewer) insights based on user preferences, quantity ofdevices providing data to analyte processor 490, and/or type of devicesproviding data to analyte processor 490. The insights may also beprocessed to determine a strong correlation for a given patient, so thatinsights presented at report 600 are those in which there is a highdegree of confidence that the insight represents the patient's actualstate. Furthermore, similar patterns may be merged, and weighting may beused to emphasize a positive insight over a negative insight (and viceversa).

In some exemplary implementations, the insights presented at insightsmodule 615 may be associated with one or more patterns being detected bypattern detector 420I. Examples of patterns include a pattern forglucose values within a target glucose range, a pattern for glucosevalues above a target glucose range, a pattern for glucose values belowa target glucose range, a pattern for detecting rapid changes from highto low glucose values (and vice versa), a pattern for high coefficientof variance, and the like. In some exemplary implementations, eachpattern at pattern detector 420I may have a single dimension, so that aseparate pattern is used to search specifically for a below rangepattern, another pattern looking for low coefficient of variance, andthe like. In some exemplary implementations, each pattern at patterndetector 420I may be statistically based and use standard descriptivestatistics. In some exemplary implementations, each pattern at patterndetector 420I may be assigned a score or a weight to indicate itsrelative importance with respect to other patterns. In some exemplaryimplementations, each pattern at pattern detector 420I may be assignedan applicable time frame over which it performs its pattern detection.

To further illustrate examples of the patterns, basic patterns may beconfigured to allow a search for certain patterns in the data, such asvalues within range, high coefficient of variance, and the like. Eachpattern may have one dimension, such as within range, with a separatepattern looking specifically for below range, another looking for lowcoefficient of variance, and the like. Each pattern may be statisticallybased and use standard descriptive statistics in the application ofpattern matching. Each pattern may be assigned scores for various rulesencoded with each pattern, such as is it positive, negative, howimportant an insight is, and the like. Each pattern may also be assigneda possible set of date ranges for which the pattern is applicable. Forexample, counting the number of times a high glucose value is followedby a low below range is a pattern that just applies to the full range.However, looking at high levels of variance can apply to a month, aweek, a day, an intraday, every other hour, hourly, and combinationsthereof. Every pattern may be assigned a minimally acceptable scorebefore it can be considered for display. Each pattern (and anyassociated rules) may be processed for a set of data for a certain timeframe, and if the pattern is applied and meets certain minimalrequirements, then the patterns are ranked according to significance. Assuch, the ranked patterns may each correspond to an insight, resultingin a ranking of the insights. For example, insights module may onlypresent a portion, such as the top five insights, although otherquantities of insights may be configured for presentation as well.

The insights module 615 may present statistical patterns over a timeframe of the report. Moreover, the specific insights representative ofstatistical patterns may be presented with the highest correlation forthat patient. For example, before a certain insight is presented,analyte processor 490 may perform post-processing, such as mergingsimilar statistical patterns together, giving a slight weighting to apositive insight over a negative one (or vice versa), and the like. Theinsights selected may be considered deterministic in the sense thatinsights are generated the same each time when the time range, patientinformation, and health data are exactly the same.

FIG. 6A-8 depicts an example of a high and low period module 620A. Thehigh and low period module 620A highlights patterns of high and lowperiods for data measured over a predefined period of time, such as 1day, 2 days, 7 days, 30-days, and other time frames as well. In thisexample, the high and low period module 620A depicts that over the last30-day period 622A, the patient providing sensor data to analyteprocessing engine 490 has had patterns of high instances of glucose(e.g., above a defined target range) detected at 622B-I. Thesehighpoints, such as high point 622E, may show a pattern of high glucoseduring the evening hours. The high and low period module 620A also showsthat no low points occurred. Should analyte processor patterns of lowglucose be detected, then the low glucose patterns may be displayed in asimilar fashion as to the high glucose patterns, although inversed. Insome exemplary implementations, analyte processor 490 including patterndetector 420I may detect the high and low events depicted at the highand low module 620A. Moreover, the detection may be based on patterns(e.g., a pattern may define a feature to identify or detect in datareceived and/or stored at system 400 and/or repository 475. Furthermore,the area under the graphed portions may be shaded to convey whether thehigh or low period exceeded a threshold. For example, the area underhigh portion 622I may be shaded to show that the high was of particularinterest (e.g., exceeded a magnitude threshold, which can be the uppertarget range threshold, for a time period exceeding a threshold value).Moreover, a plurality of thresholds (and shadings) may be used to conveyother areas of interest with respect to the graph, such as patterns ofrelatively higher highs (or longer time periods being high) or lowerlows (or longer time periods being low) being detected using differentthresholds and presented using different shadings.

FIG. 6A-9 depicts an example of a devices used module 630. The devicesused module 630 may include a textual legend 632A describing each deviceproviding data for analysis and reporting by analyte processor 490, agraphical element, such a graphical bar 632B including different shadesto present a range of time the device is actually in use, and a callout632C providing textual information regarding the percentage of time thedevice was in use over a certain time frame, such as 1 day, 2 days, 7days, 30-days, and the like. In the example of FIG. 6A-9, only a singledevice is depicted, but if a given patient provides data to analyteprocessor 490 using additional devices, those additional devices wouldalso be depicted with textual legends, graphical bars, and calloutsproviding the percentage of time the device was in use over a timeframe.

In some exemplary implementations, the devices used module 630 may alsoinclude whether a clock at the device is incorrect and whether anycorrections were made by analyte processor 490. For example, dataretriever 465 may determine that a device clock is off (or in error) bya certain amount, and report that amount to analyte processor 490. Whenthis is the case, the devices used module 630 may show this erroramount. In some exemplary implementations, the analyte processor 490shifts the data from the device to compensate for the error and providecoherency with other data, in which case the devices used module 630 mayshow the amount of time shift.

FIG. 6A-10 shows additional examples of devices used including a devicesused module 733A for a continuous glucose monitor, devices used module733B for a self-monitoring blood glucose monitor, and devices usedmodule 733C for an implementation including a plurality of devices, suchas a continuous glucose monitor and a self-monitoring blood glucosemonitor. FIG. 6A-10 also illustrates the dynamic nature of the reportand module generation as 733C is configured dynamically based on thedevices being used by the patient. FIG. 6A-10 also shows the timeshifting feature to show the differences (or errors) in the clocks ofthe devices.

FIG. 6A-11 depicts an example of a compared with module 640. Thecompared with module 640 may present a statistical comparison between apatient, such as a patient identified at 605B, and a group, such ascohorts. The compared with module 640 may include a textual legend 642Adescribing what the other group is (e.g., other patients at a clinic), agraphical element, such as a graphical bar 642B including one or moredifferent shades to present a range of values, and callouts 642C-D. Inthe example of compared with module 640, the callout 642B provides atextual indication of the percentage of time the device was in use overa certain period, such as 1 day, 2 days, 7 days, 30-days 605E, and thelike. In the example of FIG. 6A-11, the graphical bar 642B and thecallouts 642C-D compare a patient 605B and other clinical patients 642A,and this comparison is based on data obtained from the patient and otherclinical patients over a given time frame, such as 1 day, 2 days, 7days, 30 days 607F, and the like. And, the comparison relates to thetime a patient is below a target glucose range (2% as show by callout642D) and the time the other patients at the clinic are below the targetglucose range (7% as shown by callout 642C) based on data collected overa 30-day time frame, although other statistical comparisons, timeframes, and groups may be used as well. In some exemplaryimplementations, the statistical comparisons shown with respect to thecompare with module may be determined based on histograms and countsdisclosed herein. Moreover, the analyte processor 490 mayprogrammatically chose the group 642A used as a cohort, although a usermay choose the group as well. The group 642A may be selected based onmetadata representative of one or more of the following: diabetes type,age, sex, age of diagnosis, location, treatment facility, type of sensordevice used and any other factor or demographic, such as any type ofmetadata discussed herein. In some exemplary implementations, theanalyte processor 490 may programmatically select a group most similarto the patient 605B, although other selection schemes may be implementedas well.

FIG. 6A-12 depicts another example of a compared with module 674XY. Thecompared with module may be generated by the report generator to comparethe host with another user or groups of users (e.g., cohorts). In theexample at FIG. 6A-12, the patient is being compared with all thepatients within a clinic for a certain range, such as percentage ofglucose concentration levels below and above a target glucoseconcentration range. In this example, the patient is below the range 16%of the time and above the range 29% of the time, while the group isbelow the range 27% of the time and above the range 56% of the time.Although the previous comparison compared the patient with cohortsassociated with the same clinic as the patient, other groups orcomparisons may be made as well. For example, analyte processor 490 mayinclude metadata to allow comparisons based on one or more of thefollowing: diabetes type, age, gender, age of diagnosis, and the like.

In some implementations, the analyte processor 490 may programmaticallyselect a group to compare with the patient in a compare with module, andthis selection may be made in order to identify a group that is mostsimilar to the patient. Moreover, this selection may, in someimplementations, be weighted to favor a group in which the patientcompares slightly better than those in the group. And, this selectionmay be programmatically determined. For example, analyte processor 490may determine all potential cohorts that a user belongs to, and choosethe group that has a slight positive association. Analyte processor 490may also determine a difference value between all the cohorts and thepatient (e.g., by comparing the same statistic) and choose the cohortthat has the smallest difference (which may be the one that is mostsimilar to the patient). Moreover, the difference that is positive maybe selected over a difference that is negative. For example, given apatient that has a glucose concentration that is out of range 16% of thetime, a clinic having patients which are out of range 18% of the timemay be selected over other cohorts or statistics, having a negativedifference (e.g., when compared to another group having a negativedifference, such as a group of cohorts which are out of range 15% of thetime). In addition, if there is no positive difference, analyteprocessor 490 may search for slightly negative differences, so aslightly negative correlation may trump one that is significantlypositive. Returning to the previous example of glucose concentrationsout of range, if the 18% group did not exist, the analyte processor 490may select the slightly negative group at 16%.

Referring to FIG. 6A-13, the daily summary module 650 may provide amodal day view of the glucose data collected over a given time frame,such as about the past month 652A, for one or more devices providingdata for a given patent, such as patient 605B. In some implementations,the data presented to the patient may be selected to encourage thepatient (e.g., by showing the patient is doing well). The daily summarymodule 650 provides an indication of a patient's daily fluctuations overa given time frame.

In the example of daily summary module 650, the x-axis 652B represents afirst time period, such as 24 hours, and the y-axis 652C represents ameasured value, such as glucose value. Analyte processor 490 maydetermine statistics, such as percentage of time within a target glucoserange (labeled within range), a median glucose value, a middle 50% rangeof glucose value measurements, and a variability of the glucosemeasurements, based on 30-days' worth of sensor data, although othertime frames may be used as well. In the example of daily summary module650, median and quartile range are depicted, although other determinedstatistics may be presented as well. Moreover, the determined statisticsmay be categorized based on times of day, such as specific times orperiods (e.g., within epochs or more generally, such as morning,afternoon, etc.). Once categorized, the determined statistics may bepresented in a module, such as daily summary module 650.

In some exemplary implementations, the daily summary module 650 may plotover a first time frame, such as a 24-hour period, median glucose valueswith a distinctive element 652D, such as a bold line or object, and themiddle 50th percentile of glucose values are plotted with anotherdistinctive element 652E, such as light shading, and a target glucoserange is depicted by two distinctive elements 652G-H, shown as lines at70 and 180 mg/DL, for example.

When the daily summary module 650 shows self-monitoring blood glucosedata, each individual test (or a lower density form of the data) may bedisplayed as an element, such as a dot. FIG. 6A-14 depicts an example ofa daily summary module 769A configured to show self-monitoring bloodglucose data.

Referring again to FIG. 6A-13, when the daily summary module 650contains continuous blood glucose monitor data, each of the individualmeasurements may not be plotted, but instead the median±25% may beplotted, with slight shading indicating the interquartile range. In someexemplary implementations, the median and interquartile range values arecomputed at 30-minute resolutions. In some exemplary implementations,when only self-monitoring blood glucose data is plotted, the shading islighter and faint dashed lines are used, and when continuous bloodglucose monitor data is included, the shading is darker and solid linesare used to provide a distinction between the types of data sets. Insome exemplary implementations, the analyte processor 490 computes themedian and interquartile range to allow presentation at daily summarymodule 650 using a c-spline smoothing algorithm, such as theFritsch-Carlson Monotone cubic Hermite interpolation, although otherapproaches may be used as well.

In some exemplary implementations, the daily summary module 650 mayinclude a textual summarization 652F. For example, the textual summary652F may include standard descriptive statistics (e.g., median, middle50% (the IQR), variability (the CV), and percent within range), groupedby intraday time ranges, such as night, morning, midday, afternoon, andevening. Each statistic may be computed in full using all the values inthe report range, broken down by their time group, without usingsampling or weighted merges.

FIG. 6A-15 depicts another example of a daily summary module 690A. Inthis example, the daily summary module 690 depicts a daily view of datafrom a plurality of sources, which in this example corresponds toglucose data 690B, carbohydrate consumption data 690C, and insulin pumpdata 690D. The daily summary module 690A plots the glucose data 690B ina manner similar to 650. However, the carbohydrates 690B are presentedusing a plurality of shades representing a range of differentcarbohydrate consumption values (e.g., a given shade represent a givencarbohydrate consumption), and the insulin pump data 690D is present asa bar graph.

FIG. 6A-16 depicts another example of a module providing a day-to-dayview of the host/patient's analyte levels, such as glucose, and othervalues. The day-to-day details module provides a summary includingglucose levels 7100 and carbohydrate levels 7200, where the measuredintensities are presented using different graphical indicators, such asdarker shading, to show the different values of glucose orcarbohydrates. The module at FIG. 6A-16 also depicts insulin dosage 7300including amounts and times taken, with callouts, such as “1”, “2”, andso forth with a corresponding textual description of the dosage events.For example, callout “1” 7400 corresponds to textual description 7420.The module may also include a summary 7500 including statistical data,such as average glucose, total carbohydrates, total insulin, basilinsulin, and bolus insulin. The day-to-day details module provides asingle view (which can be present as a single page at a user interface)compressing a day's worth of data into a graphical abstractioncorrelating measurements among dosage, carbohydrates, and measuredglucose to enable a host/patient to readily perform a visual correlationamong carbohydrates, glucose, and dosage events.

FIG. 6A-17 depicts another example of a daily summary module. In thisexample, the daily summary module depicts a daily view of data from aplurality of sources, which in this example corresponds to glucose data,carbohydrate consumption data, and insulin pump data. The daily summarymodule at FIG. 6A-17 presents discrete data, such as SMBG data, asindicated by the data points 769A, 769B, and so forth. Moreover, theinsulin dosage depicts the actual dosage functions/profile 769C. Thedaily summary module provides a single view (which can be presented as asingle page at a user interface) compressing an extended timeframe, suchas 29 days and/or any other time period, into a graphical representationcorrelating measurements among dosage/pump data, carbohydrates, andmeasured glucose to enable a host/patient to readily perform a visualcorrelation among carbohydrates, glucose, and dosage events.

FIG. 6A-18 depicts an example of weekly summary module 660. Module 660may be implemented in a manner similar to the daily summary module 650,but present data over a 7-day period as shown by the x-axis 662A, ratherthan a 24-hour period. In some exemplary implementations, the resolutionof the data presented at the weekly summary module 660 may be calculatedover 2-hour period, although other resolutions may be used as well.

FIG. 6A-19 depicts another example of a weekly summary module 692A.Weekly summary module 692A may be implemented in a manner similar to thedaily summary module 650, but present data over a 7-day period, ratherthan a 24-hour period. In this example, the weekly summary module 692Adepicts a daily view of data from a plurality of sources, which in thisexample corresponds to glucose data, carbohydrate consumption data, andinsulin pump data. FIG. 6A-20 depicts an example of a weekly summarymodule 769B configured to show self-monitoring blood glucose data.

FIG. 6A-21 depicts an example of an over time summary module 694A. Theover time summary 694A may be similar to the daily and weekly summarymodules disclosed herein, but the over time summary 694A plots theglucose values over time, using the median value at 12 to 18 hourintervals depending on whether or not continuous glucose monitoring datais included.

FIG. 6A-22 depicts an example of a continuous glucose levels module670A. Module 670A presents in a graphical form the times when a patientwas within, above, or below a target glucose range based onretrospective data obtained over a time frame, such as 8 hours, 1 day, 2days, 7 days, 30 days, and the like. The continuous glucose levelsmodule 670A may include a legend 670B, an indication of the time period670C over which the information is plotted, and one or more graphicalelements 670D-J and so forth, such as bars, icons, and the like, for oneor more intervals within the time frame 670C.

In some exemplary implementations, the y-axis represents days of theweek during the time frame 670C and the x-axis represents times (orepochs) during the time frame. For example, graphical element 670Ddepicts a glucose value over range condition on Friday April 6th, atabout 12 AM and followed by another graphical element 670E representingthat the glucose value is within range, and graphical element 670Krepresents glucose values being under the target range on Wednesday atabout 4 AM.

In some exemplary implementations, the graphical elements for above atarget range, below a target range, and at the target range aredistinctive. For example, the above the target range 670D is depicted asa bar above the at the target range 670E, which is depicted as a line.The below the target range 670K is depicted below the at the targetrange line 670L. Moreover, the graphical elements may include value helpand other information. For example, selecting a graphical elementpresented on a computer may provide additional information regarding theglucose values, such as providing actual values for the glucose valuesabove the range as depicted at 670G (e.g., “352”) and K (e.g., “57”), orglucose values below the range.

In some exemplary implementations, the target range for glucose valuesmay be select by a host, although other entities, such as clinicians,health care providers, and system 400 may select the target range aswell. Moreover, the at the target range and below the target range maybe detected, in some exemplary implementations, by pattern detector 420Ibased on one or more patterns. For example, pattern detector 420I mayinclude a pattern that processes sensor data (or histograms and counts)stored in repository 475, identifies glucose values that are above thetarget range, and correlates dates and times of the above the rangeglucose values to enable presentation at glucose values module 676A. Thepattern detector 420I may include a pattern that processes sensor data(or histograms and counts) stored in repository 475 to identify belowthe target range glucose values. In some exemplary implementations, thepattern detector may perform the weighting and/or thresholding ofglucose values. For example, the pattern detector 420I may weigh theglucose values using a function (e.g., equalize, normalize, or map tosome other function). The weights may ensure accurate detection of highand/or low glucose events for reporting via a report module, such asdaily summary module 650. Moreover, the pattern detector 420I may useone or more thresholds to ensure that the high and/or low glucose eventsare indeed events that should be reported as high and/or low glucoseevents.

In some exemplary implementations, the pattern detector 420I may alsodetect rapid changes of glucose and correlate dates and times of therapid change to enable presentation at glucose values module 676A.Moreover, the rapid shifts may be presented in a report, such ascontinuous glucose levels module 670A.

In some exemplary implementations, the continuous glucose levels module670A may label or highlight so-called “outliers” representative of apeak high glucose value within an epoch or a peak low glucose valuewithin an epoch. Referring to continuous glucose levels module 670A, anoutlier is labeled as having a value of 352 mg/dL at 620G. Thecontinuous glucose levels module 670A may also label or highlight rapidchanges in glucose value from low to high (or vice versa).

In some exemplary implementations, the pattern detector 420I may attemptto limit the quantitative data presented at continuous glucose levelsmodule 670A. For example, presenting hundreds of outliers and rapidshifts in glucose values at continuous glucose levels module 670A mayinhibit the ability of a user to understand the data presented atcontinuous glucose levels module 670A. As such, pattern detector 420Imay process the glucose values corresponding to outliers and rapidshifts until only a subset of the outliers and rapid shifts aredetected. For example, pattern detector 420I may process the glucosevalues corresponding to outliers and rapid shifts until only about 30outliers and rapid shifts are detected, and those may be presented atcontinuous glucose levels module 670A (see, e.g., “352” at 620G, “57” at620K, and so forth.

To process sensor data (or representative counts and the like) untilonly a subset of the outliers and rapid shifts are detected, the patterndetector 420I may processes, in some exemplary implementations, all ofthe data for the time frame of the report or module, e.g., 30 days, andidentify the above the target glucose range outliers, the below thetarget glucose range outliers, and the rapid shifts. Next, patterndetector 420I may then filter some of those values based on a thresholdand a window. For example, a threshold may be applied to identify thetop 8% of the above the target glucose range outliers and the bottom 8%of the below the target glucose range outliers. The pattern detector420I may also apply a window to shift through a portion of the data todetect the rapid shifts. For example, all of the data may be processedwith a 4-hour window to identify rapid glucose shifts from low to high(and high to low). After applying the threshold and window, the patterndetector 420I may determine how many high outliers, low outliers, andrapid shifts remain. If the remaining quantity of high outliers, lowoutliers, and rapid shifts is at or below a presentation threshold(e.g., 30 outliers and rapid shifts events presented for a 30-daypresentation of continuous glucose levels), the pattern detector 420Imay provide the remaining high outliers, low outliers, and rapid shiftsto report generator 420G for presentation at continuous glucose levelsmodule 670A. If the remaining high outliers, low outliers, and rapidshifts are above the presentation threshold, the pattern detector 420Imay vary the threshold and the window size to reduce the quantity ofhigh outliers, low outliers, and rapid shifts. For example, thethresholds may be reduced from 8% to 7%, and the 4-hour may be reducedto 3.6 hours. This process may be iterative until the quantity ofoutliers and/or rapid shifts is at or below the presentation threshold.

FIG. 6A-23 depicts another example of a continuous glucose levels module670Z. In the example of FIG. 6A-23, glucose values above a target rangeare depicted with a shading 670Y that can be distinguished from ashading 670W used for glucose values below a target range and shading670X used for glucose values at a target range, although continuousglucose levels module 670Z may use positional distinctiveness amonggraphical elements as well as in the case of continuous glucose levelsmodule 670A. For example, the above the target range is depicted as anelement 670Y above the bar 670X at the target range 670X, while thebelow the target range element 670W is depicted below a corresponding atthe target range bar 670V. Although FIG. 6A-23 depicts using bothshading distinctiveness and positional distinctiveness to distinguishthe high, at, and low glucose values, each may be employed without theother.

FIG. 6A-24 depicts another example of a continuous glucose levels module770. The module 770 may be similar in some respects to the othercontinuous glucose levels modules disclosed herein, but continuousglucose levels module 770 uses shading and size to convey intensity.Specifically, at 774, the glucose level is extremely far from a targetglucose range as depicted by the darker shading at 772 and the verticalsize of the block. By contrast, at 776, the excursion from the targetglucose range is somewhat less than at 778, when the darker intensityshading drops to a lower intensity level as well as a lower verticalsize. Module 770 may also use thin bare 780 to depict periods where thehost patient is in range, and periods where there is no data may bepresented as no shading (or color) is shown, such as at 782. Althoughmodule 770 depicts the first four days of a period, module 770 maydepicts other timeframes as well. Module 770 thus abstracts the timeabove or below a range into an easy to view graphic depicting how much apatient/host is out of range with a visual indicator, such as intensity.

FIG. 6A-25 depicts another example of a continuous glucose levels module676A. The continuous glucose levels module 676A may be similar in somerespects to the continuous glucose levels module 670A and the like, butthe continuous glucose levels module 676A includes graphical elementsthat are distinctive only with respect to shading (e.g., coloring,intensity, contrast, and the like), so that glucose values above atarget range are depicted with a shading 676B that can be distinguishedfrom a shading 676C representative of glucose values below a targetrange and a shading 676D representative of glucose values at a targetrange.

FIG. 6A-26 depicts an example of a glucose meter value levels module674A. The glucose meter value levels module 674A may be similar in somerespects to the continuous glucose levels module 670A and the like, butglucose meter value levels module 674A shows discrete values associatedwith the glucose measurements (e.g., made by a self-monitoring bloodglucose meter) and may also include graphical elements, such as asubstantially circular icons 674B-C to depict a certain percentage ofthe highest glucose values above a target range and substantiallypolygonal icons 674D-E to depict a certain percentage of glucose valuesbelow a target range. The average and variability for each day may alsobe listed to the right, with a variability's displayed for a given dayonly if there are more than two glucose values provided for that day. Insome implementations, when a patient is using a self-monitoring bloodglucose device and a continuous glucose monitor and providing their datato analyte processor 490, the report generator generates two types ofreports, such as a continuous glucose level module 670Z and a discreteglucose meter values 674A. Moreover, processing by analyte processor 490is transparent in the sense that analyte processor 490 can processorboth types of data and generate reports regardless of the source or typeof data.

FIG. 6A-27 depicts another example of a glucose meter value levelsmodule 674AA. Glucose meter value levels module 674AA is similar toglucose meter value levels module 674A in many respects but includesarrows to depict above or below the range. For example, 674AB depicts anup arrow, and 674AC depicts a down arrow. Other indicators may be usedas well to depict intraday highs or lows in glucose data. Moreover,markers may be used to segment the time shown into periods, such asmorning, afternoon, evening, and night, to assist the user ininterpreting the data presented.

FIG. 6A-28 depicts a report legend 687A, which may be placed as a modulein report 700. In some implementations, the report legend module 687Amay be placed as the last module or section of the report, and mayinclude a description and explanation for various sections of thereport.

FIG. 7 depicts an example of a testing frequency of a self-monitoringblood glucose testing frequency module 700. The self-monitoring bloodglucose testing frequency module 700 includes epochs, such as weekdays702 and weekend 704, and corresponding time intervals 706 during thosetime periods. The circles represent that a host has taken a measurement.For example, circle 708A represents that the host made one or moremeasurements of blood glucose at 1 AM during the week 702, and circle708B represents that the host made one or more measurements of bloodglucose at 1 AM during the weekend 704. The larger the circle thegreater the number of measurements made at the corresponding time periodand interval. For example, circle 710B is larger than circle 708B, and,as such, circle 710B represents that the host made more blood glucosemeasurements at 8 AM, when compared to 1 AM. The report generator 420Gmay generate self-monitoring blood glucose testing frequency module 700based on counts and corresponding histograms generated by calculationengine 420H.

FIG. 8 depicts an example of patients questions module 899. The patientquestions module 899 may be configured to allow a user, such as aclinician, doctor, and the like to pose questions to a patient via areport including patients questions module 899 presented at a userinterface. If questions are asked, the patient's answers may be capturedand included in the report. The patients questions module 899 mayinclude a standard set of default questions (e.g., obtained from theAmerican Diabetes Association Standards of Care manual), which areselected by the user or programmatically by analyte processor 490.

FIG. 9 depicts another example of an insights module which includes apatient question within the same module (e.g., “a high number of glucosechecks are within range”).

FIG. 10 depicts an example of a module providing “Days of Interest.” Thedays of interest module provides a summary of information for a giventime frame. For example, over a 30 day time period, the Days of Interestmodule may show the day of the week the patient's glucose values weremost within range (e.g., Saturdays), the day of the week the patient'sglucose values were most variable (e.g., Tuesdays), the day of the weekthe patient's glucose values are higher (e.g., Sundays) or lower (e.g.,Thursdays) than the target range. This may allow a patient/host todetermine whether there are any lifestyle issues on the identified days,which may be contributing to the variability, highs, and the like.

FIG. 11 depicts an example of a process 1100 for processing analytedata, in accordance with some exemplary implementations. The descriptionof process 1100 may also refer to FIGS. 1, 4A, 4B, and 5.

At 1110, sensor data representative of an analyte measured in a host maybe received, in accordance with some exemplary implementations. Forexample, analyte processor 490 may receive sensor data, such as valuesrepresentative of blood glucose levels, from one or more devices, suchas display devices 14, 16, 18, 20, sensor system 8, data retriever 465,user interfaces 410A-C, and the like. The analyte processor 490 may alsoreceive (and/or determine) metadata associated with the analyte data,such as patient information, time of day associated with the measurementof the analyte data, and the like. Analyte processor 490 may process thereceived sensor data and/or metadata using one or more aspects ofanalyte processor 490 (e.g., authentication authorization 420, dataparser 430B, calculation engine 420H, pattern detector 420I, reportgenerator 420G, and the like as disclosed herein) and store the receivedsensor data and/or metadata in repository 475. Although the descriptionof process 1100 refers to sensor data, analyte processor 490 may processother types of data as well.

At 1120, sensor data and/or metadata received at analyte processor 490may be stored at repository 475 based on histograms and countsassociated with the received sensor data and/or metadata, in accordancewith some exemplary implementations. For example, the sensor data and/ormetadata received at 1110 may be processed by calculation engine 420H toform counts, as noted above with respect to FIG. 5. In addition, thecounts are then added to corresponding histograms (which may be storedat repository 475) based on metadata, such as a time of day associatedwith when measurement and the identity of the patient. In some exemplaryimplementations, the counts may be added to other histograms associatedwith cohorts. The repository 475 may store received sensor data,metadata, histograms, and/or counts for one or more patients (alsoreferred to as hosts) for a time frame of 8 hours, 1 day, 2 days, 7days, 30 days, or more to enable system 400 to analyze the stored dataand generate the reports disclosed herein.

At 1130, the analyte processor 490 may receive a request to perform anaction in accordance with some exemplary implementations. For example,analyte processor 490 may receive the request from a system, aprocessor, and/or a user interface, such as user interface 410A. Theaction may correspond to a request to generate a report for a certainpatient, although other actions may be requested as well. The requestmay also indicate a time frame for the report, although the time framemay be determined programmatically by system 400 as well. The requestmay indicate other aspects of the request, such as types of modules tobe included in the report. In some exemplary implementations, therequest may undergo additional processing, such as authorization,authentication, parsing, and the like at analyte processor 490.

Moreover, the request may also correspond to other actions at analyteprocessor 490. Examples of actions include storing sensor data,metadata, and any other type of data at repository 475, retrievingsensor data, metadata, and any other type of data at repository 475,configuring reports and/or modules, customizing aspects of system 400(e.g., adding devices, customizing reports, target glucose ranges, timeframes for reports, and the like).

At 1140, analyte processor 490 and/or logic 420 may evaluate the timeframe of the report, the patient's identity, and other metadataassociated with the patient (e.g., number of devices, types of devices,and the like) and analyze a portion of the sensor data associated withthe time frame. To illustrate further, logic 420 may determine that thetime frame of a report is the past 30 days, the patient's identity 605A,and that the patient is associated with a single type of continuousblood glucose monitoring device. In this example, logic 420C maydetermine descriptive measurements (e.g., one or more statistics) forthe past 30 days for the patient based on the data stored at repository475 and/or request pattern generator 420I to detect patterns for thepatient's data stored at repository 475 in the last 30 days. In someexemplary implementations, the time frame of the report and thecorresponding data evaluated for the report is selected by a user (e.g.,a patient, a clinician, and the like) or programmatically by system 400.

At 1150, logic 420 may initiate report generation at report generator420G based on the analysis performed at 1140. For example, logic 420 mayevaluate the time frame of the report, the patient's identity, and othermetadata associated with the patient (e.g., number of devices, types ofdevices, and the like), and determine the type of modules to include inthe report and the configuration of those modules. To illustratefurther, logic 420 may determine that the time frame of the report isthe past 30 days, the patient's identity 605A, and that the patient isassociated with a single continuous glucose monitoring device. In thisexample, logic 420C may determine modules customized to present ananalysis, such as statistics and patterns, for continuous blood glucosemonitoring data, such as highlights module 607A, continuous glucoselevels module 670A, and the like. In some example embodiments, themodules may be selected dynamically as described above with respect toFIG. 6A-2. The modules may be composed to form a report, such as report700 at FIG. 6A-1, to cover the patient's data over the past 30 days,although other types of report and/or modules may be used as well.Although the previous example refers to the generated report as agraphical report, the report may be text based as well or may begenerated as a machine-to-machine data exchange.

At 1160, the generated report may be provided to, for example, a userinterface, such as user interface 410A-C, another machine, and the like.

Referring again to FIGS. 4A-B, in some exemplary implementations, thesystem 400 may store health data separately from personally identifiableinformation, which is encrypted. For example, system 400 may include asecurity layer below the logic layer so that encryption occurs when datais stored to repository 475. In some exemplary implementations, theencryption is based on multiple factors, such as the host, the storagelocation (e.g., row-column information), and the like. For example, anencryption algorithm, such as the advanced encryption algorithm and thelike may be implemented. In some implementations, the encryption keysmay be split and stored on separate portions of system 400 (e.g., onanalyte processor 490, servers for the databases, and the like) withseparate user credentials for authentication.

In some exemplary implementations, the repository 475 is distributed.For example, the repository 475 may comprise a plurality of persistentstorage devices, which are distributed. Moreover, the persistent storagedevices may include one or more of relational databases, non-relationaldocument stores, non-relation key-value data stores, hierarchical filesystem—like stores (also referred to as data stores), and the like.Furthermore, the repository 475 may be replicated so that the storagedevices are geographically dispersed.

Various implementations of the subject matter described herein may berealized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof. Thecircuitry may be affixed to a printed circuit board (PCB), or the like,and may take a variety of forms, as noted. These various implementationsmay include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which may be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device.

These computer programs (also known as programs, software, softwareapplications, or code) include machine instructions for a programmableprocessor, and may be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the term “machine-readable medium” refers toany non-transitory computer program product, apparatus and/or device(e.g., magnetic discs, optical disks, memory, Programmable Logic Devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions.

To provide for interaction with a user, the subject matter describedherein may be implemented on a computer having a display device (e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor) fordisplaying information to the user and a keyboard and a pointing device(e.g., a mouse or a trackball) by which the user may provide input tothe computer. Other kinds of devices may be used to provide forinteraction with a user as well; for example, feedback provided to theuser may be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user may bereceived in any form, including acoustic, speech, or tactile input.

The subject matter described herein may be implemented in a computingsystem that includes a back-end component (e.g., as a data server), orthat includes a middleware component (e.g., an application server), orthat includes a front-end component (e.g., a client computer having agraphical user interface or a Web browser through which a user mayinteract with an implementation of the subject matter described herein),or any combination of such back-end, middleware, or front-endcomponents. The components of the system may be interconnected by anyform or medium of digital data communication (e.g., a communicationnetwork). Examples of communication networks include a local areanetwork (“LAN”), a wide area network (“WAN”), and the Internet.

Although a few variations have been described in detail above, othermodifications are possible. For example, while the descriptions ofspecific implementations of the current subject matter discuss analyticapplications, the current subject matter is applicable to other types ofsoftware and data services access as well. Moreover, although the abovedescription refers to specific products, other products may be used aswell. In addition, the logic flows depicted in the accompanying figuresand described herein do not require the particular order shown, orsequential order, to achieve desirable results. Other implementationsmay be within the scope of the following claims.

Although a few variations have been described in detail above, othermodifications are possible. For example, while the descriptions ofspecific implementations of the current subject matter discuss analyticapplications, the current subject matter is applicable to other types ofsoftware and data services access as well. Moreover, although the abovedescription refers to specific products, other products may be used aswell. In addition, the logic flows depicted in the accompanying figuresand described herein do not require the particular order shown, orsequential order, to achieve desirable results. Other implementationsmay be within the scope of the following claims.

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Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. applicationSer. No. 09/447,227 filed on Nov. 22, 1999 and entitled “DEVICE ANDMETHOD FOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No.12/828,967 filed on Jul. 1, 2010 and entitled “HOUSING FOR ANINTRAVASCULAR SENSOR”; U.S. application Ser. No. 13/461,625 filed on May1, 2012 and entitled “DUAL ELECTRODE SYSTEM FOR A CONTINUOUS ANALYTESENSOR”; U.S. application Ser. No. 13/594,602 filed on Aug. 24, 2012 andentitled “POLYMER MEMBRANES FOR CONTINUOUS ANALYTE SENSORS”; U.S.application Ser. No. 13/594,734 filed on Aug. 24, 2012 and entitled“POLYMER MEMBRANES FOR CONTINUOUS ANALYTE SENSORS”; U.S. applicationSer. No. 13/607,162 filed on Sep. 7, 2012 and entitled “SYSTEM ANDMETHODS FOR PROCESSING ANALYTE SENSOR DATA FOR SENSOR CALIBRATION”; U.S.application Ser. No. 13/624,727 filed on Sep. 21, 2012 and entitled“SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTING SENSOR DATA”; U.S.application Ser. No. 13/624,808 filed on Sep. 21, 2012 and entitled“SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTING SENSOR DATA”; U.S.application Ser. No. 13/624,812 filed on Sep. 21, 2012 and entitled“SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTING SENSOR DATA”; U.S.application Ser. No. 13/732,848 filed on Jan. 2, 2013 and entitled“ANALYTE SENSORS HAVING A SIGNAL-TO-NOISE RATIO SUBSTANTIALLY UNAFFECTEDBY NON-CONSTANT NOISE”; U.S. application Ser. No. 13/733,742 filed onJan. 3, 2013 and entitled “END OF LIFE DETECTION FOR ANALYTE SENSORS”;U.S. application Ser. No. 13/733,810 filed on Jan. 3, 2013 and entitled“OUTLIER DETECTION FOR ANALYTE SENSORS”; U.S. application Ser. No.13/742,178 filed on Jan. 15, 2013 and entitled “SYSTEMS AND METHODS FORPROCESSING SENSOR DATA”; U.S. application Ser. No. 13/742,694 filed onJan. 16, 2013 and entitled “SYSTEMS AND METHODS FOR PROVIDING SENSITIVEAND SPECIFIC ALARMS”; U.S. application Ser. No. 13/742,841 filed on Jan.16, 2013 and entitled “SYSTEMS AND METHODS FOR DYNAMICALLY ANDINTELLIGENTLY MONITORING A HOST'S GLYCEMIC CONDITION AFTER AN ALERT ISTRIGGERED”; U.S. application Ser. No. 13/747,746 filed on Jan. 23, 2013and entitled “DEVICES, SYSTEMS, AND METHODS TO COMPENSATE FOR EFFECTS OFTEMPERATURE ON IMPLANTABLE SENSORS”; U.S. application Ser. No.13/779,607 filed on Feb. 27, 2013 and entitled “ZWITTERION SURFACEMODIFICATIONS FOR CONTINUOUS SENSORS”; U.S. application Ser. No.13/780,808 filed on Feb. 28, 2013 and entitled “DEVICES, SYSTEMS, ANDMETHODS TO COMPENSATE FOR EFFECTS OF TEMPERATURE ON IMPLANTABLESENSORS”; and U.S. application Ser. No. 13/784,523 filed on Mar. 4, 2013and entitled “ANALYTE SENSOR WITH INCREASED REFERENCE CAPACITY”.

The above description presents the best mode contemplated for carryingout the present invention, and of the manner and process of making andusing it, in such full, clear, concise, and exact terms as to enable anyperson skilled in the art to which it pertains to make and use thisinvention. This invention is, however, susceptible to modifications andalternate constructions from that discussed above that are fullyequivalent. Consequently, this invention is not limited to theparticular embodiments disclosed. On the contrary, this invention coversall modifications and alternate constructions coming within the spiritand scope of the invention as generally expressed by the followingclaims, which particularly point out and distinctly claim the subjectmatter of the invention. While the disclosure has been illustrated anddescribed in detail in the drawings and foregoing description, suchillustration and description are to be considered illustrative orexemplary and not restrictive.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article ‘a’ or ‘an’ does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases ‘at least one’ and ‘one or more’ to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles ‘a’ or ‘an’ limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases‘one or more’ or ‘at least one’ and indefinite articles such as ‘a’ or‘an’ (e.g., ‘a’ and/or ‘an’ should typically be interpreted to mean ‘atleast one’ or ‘one or more’); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of ‘two recitations,’ without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to ‘at least one of A, B, and C, etc.’ is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., ‘a system having at least one ofA, B, and C’ would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to ‘at least one of A, B, or C, etc.’ is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., ‘a system having at leastone of A, B, or C’ would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase ‘A or B’ will be understood toinclude the possibilities of ‘A’ or ‘B’ or ‘A and B.’

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for generating a user interfaceassociated with sensor data representative of a glucose concentrationlevel in a host, comprising: generating, by at least one processor, aview comprising an abstraction distilled from the sensor data over atime period, wherein the view further comprises: a graphicalrepresentation comprising a plurality of different graphically distinctelements representative of whether the abstraction over the time periodis at least one of at, above, or within a predetermined glucoseconcentration level for the host, a call out comprising value help forthe graphical representation, and a textual legend comprising adescription of the graphical representation and the abstraction; andproviding, by the at least one processor, the view as a module.
 2. Themethod of claim 1, wherein the time frame is selected from the groupconsisting of 8 hours, 1 day, 2 days, 7 days, and 30 days.
 3. The methodof claim 1, wherein the graphically distinct elements use at least oneof different shading or position to represent whether the abstractionover the time period is at least one of at, above, or within thepredetermined glucose concentration level for the host.
 4. The method ofclaim 1, wherein the call out comprising the value help is coupled toone of the different graphically distinct elements and provides anumerical value for the one of the different graphically distinctelements.
 5. The method of claim 1, wherein the view is configured forpresentation as a single page within the user interface.
 6. The methodof claim 1, wherein the module includes metadata.
 7. The method of claim6, wherein the metadata includes information to enable selection of themodule from among a plurality of modules.
 8. The method of claim 1further comprising presenting the view.
 9. An apparatus comprising atleast one processor; and at least one memory including code which whenexecuted by the at least one processor provides operations comprising:generating a view comprising an abstraction distilled from sensor dataover a time period, wherein the sensor data represents a glucoseconcentration level in a host, and wherein the view further comprises: agraphical representation comprising a plurality of different graphicallydistinct elements representative of glucose variability including a rateof change of the sensor data over the time period, a call out comprisingvalue help for the graphical representation, and a textual legendcomprising a description of the graphical representation and theabstraction; and providing the view as a module.
 10. The apparatus ofclaim 9, wherein the time frame is selected from the group consisting of8 hours, 1 day, 2 days, 7 days, and 30 days.
 11. The apparatus of claim9, wherein the graphically distinct elements use different shading toindicate whether the abstraction over the time period represents atleast one of a high degree of stability, a low degree of stability, or amoderate degree of stability.
 12. The apparatus of claim 9, wherein thecall out comprises a numerical value for at least one of the differentgraphically distinct elements.
 13. The apparatus of claim 9, wherein theview is configured for presentation as a single page within the userinterface.
 14. A non-transitory computer-readable storage mediumincluding program code, which when executed by at least one processorprovides operations comprising: generating at least one of a pluralityof reporting modules configured to provide a view comprising anabstraction distilled from sensor data over a time period, wherein thesensor data represents a glucose concentration level for a host, andwherein the view further comprises: a graphical representationcomprising a plurality of different graphically distinct elementsrepresentative of the glucose concentration level for the host, a callout comprising value help for the graphical representation, and atextual legend comprising a description of the graphical representationand the abstraction; and providing the view as the at least one of aplurality of reporting modules.
 15. The non-transitory computer-readablestorage medium of claim 14, wherein the graphically distinct elementsare representative of whether the abstraction over the time period is atleast one of at, above, or within a predetermined glucose concentrationlevel for the host.
 16. The non-transitory computer-readable storagemedium of claim 14, wherein the graphically distinct elements arerepresentative of glucose variability over the time period.
 17. Thenon-transitory computer-readable storage medium of claim 14, wherein theplurality of modules include metadata to enable selection of the atleast one of the plurality of modules from among a plurality of modules.18. The non-transitory computer-readable storage medium of claim 14,wherein at least one of the plurality of reporting modules comprises aninsights module including at least one of a plurality of insightsdetected based on a pattern and selected for presentation based on aranking according to at least one of a user preference, a quantity ofdevices providing data, one or more types of devices providing data, anda correlation with the host.
 19. The non-transitory computer-readablestorage medium of claim 14, wherein at least one of the plurality ofreporting modules comprises a compare with module presenting astatistical comparison between the host and a group selected based onaggregate data representative of one or more of the following: adiabetes type associated with the group, an age associated with thegroup, a gender associated with the group, an age of diagnosisassociated with the group, a location associated with the group, or atreatment facility associated with the group.
 20. The non-transitorycomputer-readable storage medium of claim 14, wherein at least one ofthe plurality of reporting modules comprises at least one of a patientinformation module, a highlights module, a high and low period ofglucose module, a devices used module, a compared with module, a dailysummary module, a weekly summary module, an over time summary module, acontinuous glucose levels module, a report legend module, or a testingfrequency module.